Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing) by Eneko Osaba (editor), Xin-She Yang (editor) (z-lib.org).pdf

SpringerTracts in Nature-Inspired Computing
Eneko Osaba
Xin-She Yang Editors
Applied
Optimization
and Swarm
Intelligence

Springer Tracts in Nature-Inspired Computing
Series Editors
Xin-She Yang, School of Science and Technology, Middlesex University, London,
UK
Nilanjan Dey, Department of Information Technology, Techno India College of
Technology, Kolkata, India
Simon Fong, Faculty of Science and Technology, University of Macau, Macau,
Macao

Thebookseriesisaimedatprovidinganexchangeplatformforresearcherstosumma-
rize the latest research and developments related to nature-inspired computing in
the most general sense. It includes analysis of nature-inspired algorithms and tech-
niques, inspiration from natural and biological systems, computational mechanisms
and models that imitate them in various fields, and the applications to solve real-world
problems in different disciplines. The book series addresses the most recent inno-
vations and developments in nature-inspired computation, algorithms, models and
methods, implementation, tools, architectures, frameworks, structures, applications
associated with bio-inspired methodologies and other relevant areas.
The book series covers the topics and fields of Nature-Inspired Computing, Bio-
inspired Methods, Swarm Intelligence, Computational Intelligence, Evolutionary
Computation, Nature-Inspired Algorithms, Neural Computing, Data Mining, Arti-
ficial Intelligence, Machine Learning, Theoretical Foundations and Analysis, and
Multi-Agent Systems. In addition, case studies, implementation of methods and algo-
rithms as well as applications in a diverse range of areas such as Bioinformatics, Big
Data, Computer Science, Signal and Image Processing, Computer Vision, Biomed-
ical and Health Science, Business Planning, Vehicle Routing and others are also an
important part of this book series.
The series publishes monographs, edited volumes and selected proceedings.
More information about this series at http://www.springer.com/series/16134

Eneko Osaba · Xin-She Yang
Editors
Applied Optimization
and Swarm Intelligence

Editors
Eneko Osaba
Tecnalia Research and Innovation
BRTA (Basque Research and Technology
Alliance)
Derio, Spain
Xin-She Yang
Department of Design Engineering
and Mathematics
School of Science and Technology
Middlesex University
London, UK
ISSN 2524-552X ISSN 2524-5538 (electronic)
Springer Tracts in Nature-Inspired Computing
ISBN 978-981-16-0661-8 ISBN 978-981-16-0662-5 (eBook)
https://doi.org/10.1007/978-981-16-0662-5
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore
Pte Ltd. 2021
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether
the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse
of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and
transmission or information storage and retrieval, electronic adaptation, computer software, or by similar
or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book
are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or
the editors give a warranty, expressed or implied, with respect to the material contained herein or for any
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.
The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,
Singapore

Preface
Optimization is one of the most intensively studied fields closely related to many
other fields such as artificial intelligence, data mining, machine learning, engineering
design, and industrial applications. A remarkable amount of papers and studies are
published year by year, focusing on solving different types of problems and appli-
cations. Usually, the efficient tackling of such optimization problems requires huge
computational resources. A number of different approaches can be found in the
literature to address these issues in an efficient way.
Inrecentyears,swarmintelligenceandnature-inspiredoptimizationmethodshave
demonstrated to have promising performance when dealing with a great variety of
optimization problems in different contexts. To cite a few, some common applications
are related to logistics, energy, job-shop scheduling, vehicle routing, or medicine.
There are some exemplary studies of the heterogeneous research activities around
this vibrant field.
More specifically, swarm intelligence represents one of the most highly active
research areas in the current optimization community, with more than 16,000 contri-
butions published since the beginning of the 2000s. A clear upward tendency can
be easily deduced by analyzing the most commonly used scientific libraries. Specif-
ically, based on the renowned Scopus database, scientific production regarding this
area grows at a remarkable rate from nearly 400 papers in 2007 to more than 2000
papers in 2018 and 2019.
Therefore, accompanying the interests in the current optimization community, this
book strives to provide a timely review concerning theories and recent developments
of swarm intelligence methods and their applications in both synthetic and real-
world optimization problems. The topics include ensemble evolutionary methods,
numerical associations rule mining, time series forecasting, sport-inspired meta-
heuristics, robotics, real-world optimization frameworks, deep excavation systems,
and many others. Therefore, this timely book can serve as a reference for researchers,
lecturers, and practitioners interested in swarm intelligence, optimization, data
mining, machine learning, and industrial applications.
v

vi Preface
Wethankalltheindependentreviewersfortheireffortsandconstructivecomments
during the whole peer-review process. We also thank Dr. Aninda Bose and Springer
for their assistance and professionalism.
Derio, Spain
London, UK
December 2020
Eneko Osaba
Xin-She Yang

Contents
1 Applied Optimization and Swarm Intelligence: A Systematic
Review and Prospect Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Eneko Osaba and Xin-She Yang
2 A Review on Ensemble Methods and their Applications
to Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Carlos Camacho-Gómez, Sancho Salcedo-Sanz, and David Camacho
3 A Brief Overview of Swarm Intelligence-Based Algorithms
for Numerical Association Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . 47
Iztok Fister Jr. and Iztok Fister
4 Review of Swarm Intelligence for Improving Time Series
Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Aziz Ouaarab, Eneko Osaba, Marwane Bouziane, and Omar Bencharef
5 Soccer-Inspired Metaheuristics: Systematic Review of Recent
Research and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 Formal Cognitive Modeling of Swarm Intelligence
for Decision-Making Optimization Problems . . . . . . . . . . . . . . . . . . . . . 103
Almudena Campuzano, Andrés Iglesias, and Akemi Gálvez
7 Nature-Inspired Optimization Algorithms for Path Planning
and Fuzzy Tracking Control of Mobile Robots . . . . . . . . . . . . . . . . . . . 129
Radu-Emil Precup, Emil-Ioan Voisan, Radu-Codrut David,
Elena-Lorena Hedrea, Emil M. Petriu, Raul-Cristian Roman,
and Alexandra-Iulia Szedlak-Stinean
8 A Hardware Architecture and Physical Prototype
for General-Purpose Swarm Minirobotics: Proteus II . . . . . . . . . . . . . 149
Nureddin Moustafa, Andrés Iglesias, and Akemi Gálvez
vii

viii Contents
9 Evolving a Multi-objective Optimization Framework . . . . . . . . . . . . . 175
Antonio J. Nebro, Javier Pérez-Abad, José F. Aldana-Martin,
and José García-Nieto
10 Swarm Intelligence Based Optimum Design of Deep
Excavation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
E. Uray and S. Çarbaş

Editors and Contributors
About the Editors
Eneko Osaba works at TECNALIA as a senior researcher in the ICT/OPTIMA
area. He received the B.S. and M.S. degrees in computer sciences from the Univer-
sity of Deusto, Spain, in 2010 and 2011, respectively. He obtained his Ph.D. degree
on artificial intelligence in 2015 in the same university, being the recipient of a
Basque Government doctoral grant. Throughout his career, he has participated in the
proposal, development and justification of more than 25 local and European research
projects. Additionally, Eneko has also participated in the publication of 125 scientific
papers (including more than 25 Q1). He has performed several stays in universities
of UK (Middlesex University), Italy (Universitá Politecnica delle Merche) and Malta
(University of Malta). Eneko has served as a member of the program committee in
more than 45 international conferences. Furthermore, he has participated in orga-
nizing activities in more than 12 international conferences. Besides this, he is a
member of the editorial board of International Journal of Artificial Intelligence,
Data in Brief and Journal of Advanced Transportation, and he has acted as the guess
editor in journals such as Journal of Computational Science, Neurocomputing, Logic
Journal of IGPL, Advances in Mechanical Engineering Journal, Swarm and Evolu-
tionary Computation and IEEE ITS Magazine. In his research profile, it can be found
a 19 h-index with 1450 cites in google scholar. Additionally, Eneko was an individual
ambassador for ORCID along 2017–2018. Finally, he has nine intellectual property
registers, granted by the Basque Government, and he has two European patents under
review.
Xin-She Yang obtained his D.Phil. in applied mathematics from the University of
Oxford. He then worked at Cambridge University and National Physical Labora-
tory (UK) as Senior research Scientist. Now he is a reader/professor at Middlesex
University London, and the IEEE CIS chair for the task force on business intelli-
gence and knowledge management. With more than 20 years’ teaching and research
experience, he has authored 15 books and edited 25 books. He has published more
than 250 peer-reviewed research papers with nearly 55,000 citations. According to
ix

x Editors and Contributors
Clarivate Analytics/Web of Sciences, he has been on the prestigious list of highly
cited researchers for five consecutive years (2016–2020).
Contributors
José F. Aldana-Martin Dpto. de Lenguajes y Ciencias de la Computation, Ada
Byron Research Building, University of Málaga (Spain), Málaga, Spain
Omar Bencharef Faculty of Sciences and Techniques, Cadi Ayyad University,
Marrakesh, Morocco
Marwane Bouziane Faculty of Sciences and Techniques, Cadi Ayyad University,
Marrakesh, Morocco
Carlos Camacho-Gómez Universidad Politécnica de Madrid, Madrid, Spain
David Camacho Universidad Politécnica de Madrid, Madrid, Spain
Almudena Campuzano Research Master’s Programme in Brain and Cognitive
Sciences, Faculty of Social and Behavioural Sciences, Science Park Campus
(Amsterdam), University of Amsterdam, Amsterdam, The Netherlands
S. Çarbaş Department of Civil Engineering, Karamanoglu Mehmetbey University,
Karaman, Turkey
Radu-Codrut David Department of Automation and Applied Informatics,
Politehnica University of Timisoara, Timisoara, Romania
Iztok Fister Faculty of Electrical Engineering and Computer Science, University
of Maribor, Maribor, Slovenia
Iztok Fister Jr. Faculty of Electrical Engineering and Computer Science, Univer-
sity of Maribor, Maribor, Slovenia
José García-Nieto Dpto. de Lenguajes y Ciencias de la Computation, Ada Byron
Research Building, University of Málaga (Spain), Málaga, Spain
Akemi Gálvez Department of Applied Mathematics and Computational Sciences,
University of Cantabria, Santander, Spain;
Department of Information Sciences, Faculty of Sciences, Toho University,
Funabashi, Japan
Elena-Lorena Hedrea Department of Automation and Applied Informatics,
Andrés Iglesias Department of Information Sciences, Faculty of Sciences, Toho
University (Funabashi), Ota City, Japan;
Department of Applied Mathematics and Computational Sciences, University of
Cantabria, Santander, Spain

Editors and Contributors xi
Nureddin Moustafa Department of Applied Mathematics and Computational
Sciences, University of Cantabria, Santander, Spain
Antonio J. Nebro Dpto. de Lenguajes y Ciencias de la Computation, Ada Byron
Eneko Osaba TECNALIA, Basque Research and Technology Alliance (BRTA),
Donostia-San Sebastian, Spain
Aziz Ouaarab Faculty of Sciences and Techniques, Cadi Ayyad University,
Marrakesh, Morocco
Emil M. Petriu School of Electrical Engineering and Computer Science, Univer-
sity of Ottawa, Ottawa, Ontario, Canada
Radu-Emil Precup Department of Automation and Applied Informatics,
Javier Pérez-Abad Dpto. de Lenguajes y Ciencias de la Computation, Ada Byron
Raul-Cristian Roman Department of Automation and Applied Informatics,
Sancho Salcedo-Sanz Universidad de Alcalá, Escuela Politécnica Superior, Alcalá
de Henares, Spain
Alexandra-Iulia Szedlak-Stinean Department of Automation and Applied Infor-
matics, Politehnica University of Timisoara, Timisoara, Romania
E. Uray Department of Civil Engineering, KTO Karatay University, Konya, Turkey
Emil-Ioan Voisan Department of Automation and Applied Informatics, Politehnica
University of Timisoara, Timisoara, Romania
Xin-She Yang School of Science and Technology, Middlesex University, Hendon
Campus, London, UK

Chapter 1
Applied Optimization and Swarm
Intelligence: A Systematic Review and
Prospect Opportunities
1 Introduction
Swarm Intelligence (SI, [1]) has arisen as one of the most studied areas within the
wider artificial intelligence field. In fact, SI is the most high-growing branch on the
current bio-inspired computation community [2]. Most renowned scientific databases
support this affirmation, showing a clear crescendo trend in the number of works
published around this scientific topic in last years [3]. In a nutshell, SI can be defined
as a specific stream of bio-inspired computation, based on the collective intelligence
inherent to large populations of agents with simple behavioral patterns of interaction
and communication. Arguably, the principal inspirations behind the first conception
and subsequent establishment of SI are the well-known Particle Swarm Optimization
(PSO, [4]) and Ant Colony Optimization (ACO, [5]). These both algorithms trigger
the success of SI, being the basis and main influence for the research carried out
thereafter.
The bountiful research conducted year by year around SI showcases the interest
it arouses in practitioners and researchers, which are attracted to this field because of
the capability and adaptability of such solvers for obtaining near-optimal solutions
on a wide range of high-demanding situations and problems. Precisely, the ability
of these methods for efficiently solving both real-world and academic problems is
one of the principal advantages of SI-based metaheuristics. In this regard, the con-
E. Osaba (B)
TECNALIA, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 2,
20009 Donostia-San Sebastian, Spain
e-mail: eneko.osaba@tecnalia.com
X.-S. Yang
School of Science and Technology, Middlesex University, Hendon Campus, London
NW4 4BT, UK
e-mail: x.yang@mdx.ac.uk
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
E. Osaba and X.-S. Yang (eds.), Applied Optimization and Swarm Intelligence,
Springer Tracts in Nature-Inspired Computing,
https://doi.org/10.1007/978-981-16-0662-5_1
1

2 E. Osaba and X.-S. Yang
solidation of the SI paradigm was materialized after decades of successful scientific
work, performed by a nourished and remarkably active community and as a result
of the conduction of several sequential groundbreaking studies, which help to the
foundation of some essential concepts.
As mentioned above, the proposal of PSO and ACO supposed the decisive mile-
stone for the final constitution of the SI knowledge field. In any case, an additional
concept was necessary for establishing the main roots on which further research
rests. This primeval landmark emerged on horseback between the 60s and 70s. In
those years, researchers such as Fogel, Schwefel, and Rechenberg published their
preliminary practical and theoretical studies focused on Evolutionary Programming
(EP) and Evolving Strategies (ES) [6–8]. An additional influential concept was pre-
sented some years later by John H. Holland: Genetic Algorithm (GA, [9]). This
widely-known metaheuristic was firstly conceived in 1975, paving the way to the
field referred today as bio-inspired computation. All these three mentioned concepts
(that is, ES, EP, and GA) were unified in the 90s in a concept coined as Evolutionary
Computation.
In this way, both the establishment of Evolutionary Computation and the for-
mulation of the above-mentioned ACO and PSO were paramount elements for the
formulation of the paradigm known today as SI. Since then, SI techniques have been
used for giving a response to a wide variety of problems and scientific concerns. In
this paper, we systematically outline the latest advances conducted in the field of
SI, and its application to four of the most intensively studied fields: transportation,
energy, medicine, and industry. Furthermore, despite mentioning works dedicated to
the solving of academic problems, we put special attention to those works devoted
to the application of SI techniques on real-world optimization problems. Lastly, an
equally important contribution of this study is our personal envisioned status of this
area, which we present in the form of challenges and open opportunities that remain
insufficiently addressed to date.
In line with this, we are aware that the work around SI is such numerous that it is
not possible to gather in a single chapter. For this reason, we refer interested readers
to some highly valuable survey papers, specifically devoted to review the adoption
of SI techniques to some concrete domains. In [10], for instance, a survey devoted
to Portfolio Optimization is introduced. In [11], an additional survey is proposed,
dedicated in this case to geophysical data inversion. The latest findings on Intelligent
Transportation Systems can be found in [12], while in [13], authors summarize the
work done regarding the feature selection problem. An additional significant work
can be found in [14], which presents a comprehensive review of SI metaheuristics for
dynamic optimization problems. Further in-depth surveys can be found in [15–17].
This paper is structured in the following way: in Sect.2 we present a short general
overview on SI, highlighting the main algorithms that have capitalized the attention
of the community in recent years. Section 3 is committed to the systematic overview
of the recent research done around four of the most intensively studied application
fields: transportation, energy, medicine, and industry. We discuss in Sect.4 some of
the unsolved challenges in the applications above pointed. Lastly, Sect.5 concludes
this study with a general outlook for a wide audience.

1 Applied Optimization and Swarm Intelligence … 3
2 Swarm Intelligence in Recent Years
Since the birth of the SI paradigm, a myriad of methods has been proposed in the
literature inheriting the main concepts of the primeval PSO and ACO. Thus, for
the design and development of these novel techniques, a plethora of inspirational
sources have been used. All these sources can be mainly separated into three different
categories:
• Physical processes: Physical phenomena have been served as inspiration for the
formulation of many SI metaheuristic schemes. All these methods cover a broad
spectrum of processes such as electromagnetic theory (Electromagnetism-like
Optimization, [18]), black holes (Black Hole Optimization, [19]), optics systems
(RayOptimization,[20]),meteorologicalphenomena(Hurricane-BasedOptimiza-
tionAlgorithm,[21]),gravitationaldynamicsandkinematics(GravitationalSearch
Algorithm, [22]), or the natural flow of the water (Water Cycle Algorithm, [23]).
A recent survey revolving in this concrete type of solvers can be found in [24].
• Behavioral patterns in nature: In this category, we can additionally find two dif-
ferent branches. The first one, which is also the most refereed one in the literature,
is the behavioral patterns of animals. This specific stream has gathered a signif-
icant attention from the community in recent years, leading to the proposal of
diverse approaches base on creatures as cuckoos (Cuckoo Search, CS, [25]), bees
(Artificial Bee Colony, ABC, [26]), fireflies (Firefly Algorithm, FA, [27]), bats
(Bat Algorithm, BA, [28]), whales (Whale Optimization Algorithm, [29]), corals
[30, 31], wolfs (Grey Wolf Optimizer, [32]), cats (Cat Swarm Optimization, [33]),
or Monkeys (Spider Monkey Optimization, [34]). In another vein, the second of
thesecategories regards biological processes, suchas thechemotacticmovement of
bacteria (Bacterial Foraging Optimization Algorithm, [35]), pollination process of
flowers (Flower Pollination Algorithm, [36]), the geographical distribution of bio-
logical organisms (Biogeography-Based Optimization, [37]), or natural immune
systems (Immune-Inspired Computational Intelligence, [38]).
• Political and social behaviors: Another important source of inspiration for the pro-
posal of novel successful methods are the political philosophies and social-human
behaviors. On the one hand, we can find promising adaptations of political concepts
such as imperialism (Imperialist Competitive Algorithm, [39]), parliamentary sys-
tems (Parliamentary Optimization Algorithm, [40]), ideological patters (Ideology
Algorithm, [41]), or anarchy (Anarchic Society Optimization, [42]). On the other
hand, social and human attitudes and procedures have also been used as a source
of inspiration for many methods. Some of this stimulus are the mutual interactions
of human societies (Society and Civilization, [43]), structure of societies (Hier-
archical Social Metaheuristic [44]), cultural patterns (Cultural Algorithm, [45]),
sports and games (Golden Ball, [46, 47]; World Cup Competition, [48]; Soccer
League Competition Algorithm, [49]), or intellectual procedures (Brain Storm
Optimization Algorithm, [50], Wisdom of Artificial Crowds [51]).
All these algorithms and their application to a wide variety of problems and real-
world situations have yielded very rich and abundant literature. As mentioned in the

introduction, the produced research material is such big that no paper can gather
all the published papers regarding each algorithm and each application field. In any
case, with the main intention of superficially outlining the state of the area, we will
mention in this section some of the latest applications of some of the most renowned
algorithms. After that, in the following section, we will revolve around the four
application streams mentioned in the introduction.
For this specific section, six different methods have been chosen: Particle Swarm
Optimization, Ant Colony Optimization, Artificial Bee Colony, Firefly Algorithm,
Cuckoo Search, and Bat Algorithm. As can be deduced, this election has not been
made in an arbitrary way. We have opted for these algorithms after an exhaustive
search in the most reputable scientific databases. As a result, these six algorithms are
among the ones that have produced the most scientific material.
Regarding PSO, many valuable works have been published in the last few years.
In [52], for example, Wang et al. presented a PSO-based clustering algorithm for
the resolution of solving the well-known wireless sensor network problem. Authors
in [53] introduced a Surrogate-assisted hierarchical PSO, which is tested over seven
benchmark functions of dimensions 30, 50, and 100. Also interesting is the research
proposed in [54], in which a hybrid Firefly and PSO metaheuristic are modeled for
the resolution of expensive numerical problems. An enhanced PSO is published by
Kiran in [55], endowing the algorithm with a novel update mechanism. Chen et al.
introduced in [56] a so-called biogeography-based learning PSO, in which particles
are updated using the combination of its personal best position and best positions
of the rest of the particles, employing the migration mechanism of biogeography-
based optimization [37]. An additional interesting study is proposed in [57], where
authors implement a distributed and enhanced version of the PSO for tackling the
flexible Job-shop Scheduling Problem. Also distributed is the PSO published in [58],
designed for dealing with Large-Scale Optimization problems. In that paper, the PSO
is endowed with an Adaptive Granularity Learning module. An enhanced PSO is also
developed in [59], in which the employment of a levy-flight mechanism is explored
for the efficient movement of particles along the search space. Further examples of
recent PSO high-quality studies can be found in [60–62].
In another vein, ACO has been the most intensively used SI method in the last few
years, continuing the growing trend built since its proposal in the nineties. As recent
influential work, we can highlight the research conducted by Deng et al. in [63], in
which an improved ACO is proposed for solving scheduling problems. The main
mechanisms that build up this improved version of the ACO are a multi-population
strategy, a co-evolutionary mechanism, a novel pheromone updating procedure, and
a pheromone diffusion operator. Interesting is also the work introduced in [64], in
which the efficiency of an ACO is tested for predicting financial crises. Also valuable
is the application presented in [65], focused in that case on the block relocation
problem. Asghari and Navimipour proposed in [66] an inverted ACO, modeled for
the resource discovery in the peer-to-peer networks. An interesting adaptive variant
of the ACO is designed in [67], which has the main purpose of dealing with the
challenging multimodal continuous optimization problems. A further novel variant
of the ACO is developed by Zhou et al. in [68]. In that case, a parallel implementation

of the method is implemented, with the main intention of easing its execution on
multi-core SIMD CPUs. For additional works focused on ACO, we recommend the
survey recently published by Dorigo and Stützle in [69].
In relation to ABC, many interesting advances have been presented in recent years.
In [70], for example, Gao et al. proposed an improved version of the canonical ABC,
based on the adoption of two novel updating equations for boosting the performance
of both onlooker and employed agents. Furthermore, a new search direction mecha-
nism is also proposed, along with an intelligent learning procedure for accelerating
the overall convergence of the metaheuristic. In [71], a self-adaptive variant of the
ABC is modeled and tested over global optimization problems. Also interesting is
the work published in [72], which revolves around the implementation of a tech-
nique hybridizing an ABC and the well-known Differential Evolution. An additional
novel hybridization of the ABC is unveiled in [73]. In that case, a Shuffled ABC
is developed, which is focused on the embedding of the principal concepts of the
shuffled frog-leaping algorithm on the main structure of the basic ABC. In [74],
Gorkemli and Karaboga present a so-called quick semantic ABC programming for
solving symbolic regression problems. Also influential is the novel variant of the
ABC introduced by Li and Yang in [75], which delves on the concept of providing
bees with the memory ability of natural honeybees. For readers interested on further
works around ABC solver, we recommend additional studies such as [76–78].
Turning our attention to more recent techniques, we focus our efforts now on the
CS, which has enjoyed great success in the last few years. In [79], an interesting
variant of the CS can be found, devoted to the solving of multimodal optimization
problems. The main novelties of this improved CS, coined as Reinforced CS, are
materialized through the adoption of three different strategies: self-adaptive strat-
egy, Patron-Prophet concept, and modified selection strategy. In [80], a benchmark
of bio-inspired methods is adapted for dealing with community detection problems
over weighted and directed networks. We can find a discrete CS between all the
considered solvers, being among the best-performing ones. Interesting is also the
work proposed in [81], which principal contribution is the implementation of three
different adaptive CS techniques based on dynamically increasing switching param-
eters. Pandey et al. introduced in [82] a valuable practical application of the CS,
used to find the optimum cluster-heads from the sentimental contents of the Twit-
ter dataset. In [83], the CS is employed for the border reconstruction of medical
images with rational curves. A hybrid CS is presented by Wang et al. in [84] for
global numerical optimization. In that case, CS is combined with a Harmony Search
metaheuristic. Further interesting work is proposed in [85], which explores the mod-
eling of a novel variant of the CS for improving the distance vector-hop performance
for cyber-physical systems. Moreover, some recently published theoretical works
revolving around different aspects of CS can also be found in works such as [86–88].
It is noteworthy to highlight here an interesting variant of the CS, which has served
as guidance for several studies recently published: the Random-Keys CS [89]. The
main motivation behind the formulation of this variant is to efficiently deal with the
transition from continuous to discrete spaces, by avoiding the passage of traditional
adaptation operators. Some applications of the Random-Keys CS can be found in

[90, 91]. Finally, for additional recent works focused on CS, we recommend the
following surveys and practical works: [92, 93].
On other matter, Firefly Algorithm is another recent SI method that has gathered
abundant scientific material around its figure. In last years, a plethora of high-quality
works have been published focused on this technique, like the one introduced by
Wang et al. in [94, 95]. In that papers, the authors design several improved variants
of the canonical FA. In the former one, the so-called FA with random attraction, a
randomly attracted model is used for the movement of individuals along the search
space. On the second of these works, a FA with neighborhood attraction is imple-
mented, in which each firefly is attracted by other brighter individuals selected from
a predefined neighborhood instead of choosing those from the complete population.
Another enhanced version of the FA is presented in [96], endowing the technique
with a courtship learning mechanism. In [97], a hybrid method is developed, combin-
ing the advantages of both the FA and Differential Evolution. A valuable improved
FA is modeled in [98], devoted in that case to the color image segmentation problem.
In [99], a self-adaptive variant of the FA is introduced for the parametric learning of
associative functional networks. A further valuable application of FA is introduced
in [100], which is the solving of the well-known resource allocation problem. For
additional recent papers focused on FA, we refer readers to works such as [101, 102].
The last of the algorithms outlined in this section is the BA. Firstly introduced
in 2010, this technique has caught the attention of a significant except of the SI
community. In [103], Chakri et al. proposed an enhanced version of the canonical
BA, endowing individuals with a movement strategy based on directional echoloca-
tion of bats. In [104], a chaotic BA is presented for tackling the economic dispatch
problem. The same chaotic BA is used in [105] for dealing with multi-level image
thresholding purposes. An interesting distributed version of the BA is introduced
in [106], called Island BA. The main motivation of that formulation is to empower
the capability of the method for controlling its diversity. Another distributed BA is
proposed in [107]. In that paper, a co-evolutionary BA is designed for the resolution
of evolutionary multitasking scenarios. Thanks to the co-evolutionary feature of that
algorithm, several tasks are simultaneously solved, obtaining promising results in all
of them. Interesting is also the research presented in [108], devoted to the implemen-
tation of a BA enhanced with a triangle-flipping strategy for the updating of the bats’
velocity. Apart from these works, different hybrid methods have been introduced in
the last years, having the BA as one of the combining elements. One example can
be seen in [109], using the differential evolution algorithm for the hybridization.
In the research carried out at [110], focused on multi-objective optimization, the
method considered for being combined with BA is the random black hole model.
Further hybrid techniques can be found in [111–113]. Readers interested on the BA
are referred to additional valuable scientific work such as [114, 115].
As have been seen along this section, the literature behind SI and its methods is
reallyabundantandfullofhigh-qualityworks.Indeed,wehaveonlymentionedalittle
excerpt of the recent literature around some of the most successful metaheuristics. As

pointed before, gathering all the related literature could be not possible for a single
paper, for this reason, we recommend again related surveys such as [14, 116] for
further information on this knowledge area.
3 Swarm Intelligence and Applied Optimization
As mentioned, we briefly highlight in this section the most recent advances around
Swarm Intelligence and four of the most intensively studied applied optimization
fields. Thus, in the following Sect.3.1, we focus on transportation and logistics cases,
whose advances have maintained a remarkable pace over the last years. We continue
in Sect.3.2, by systematically reviewing recent studies carried out around the field
of industry, an area which has gained a significant momentum in the last years.
We also outline recent investigations performed in medicine, which is a field that
clearly benefited from recent technological and computational advances (Sect.3.3).
We finish this segment of the paper turning our attention to the latest advances related
to energy (Sect.3.4).
3.1 Swarm Intelligence in Transportation and Logistics
In the current community, two of the most studied problems related to transportation
and logistics are focused on traffic flow prediction and route planning [12]. For
this reason, this subsection revolves around works framed in these two real-world
application areas.
First, many cities in the world have problems with traffic congestions. For tack-
ling this problem and mitigating its impact, short-term traffic forecasting has been
widely studied in many areas of ITS in the last decades. The main objectives of
forecast applications are to offer accurate information to the users, and be employed
for signal optimization. Besides that, this information can also help travelers to plan
their routes avoiding the most congested segments and other traveling incidences.
As a consequence, avoiding these congested paths, users contribute to decrease the
congestion severity. In the work presented in [117], for example, the authors pro-
pose an innovative algorithm integrated with PSO and Neural Networks (NNs) to
develop short-term traffic flow predictors. An additional hybrid approach combining
a NN with a GA and locally weighted regression methods is presented in [118] for
lane-based short-term urban traffic prediction. Specifically, the developed methods
are applied to predict short-term traffic for four lanes of an urban road in Beijing,
China. An additional hybrid method called GACE is developed in [119] combining
a GA with cross-entropy philosophy. In that work, the efficiency of the implemented
approach to predict congestion is tested in a 9-km-long stretch of the I5 freeway in
California, with three different time horizons: 5, 15, and 30 min.

The PSO is an algorithm frequently used in the last years in this context. Also, in
this case, this method is often used in combination with other algorithms, in order to
enhance the overall performance of the system. In [120], for example, a hybridization
between a PSO and a Support Vector Machine is presented. An additional hybrid
approach can be found in [121] using a PSO in combination with a Grey NN. In
that work, a system is implemented for predicting the average speed of vehicles on
Barbosa road in Macao.
Turning now our attention to problems related to route planning, in [122] a com-
plex logistic system is designed, comprised by a sustainable supply chain network,
which is connected to a distribution route planning system. For solving both prob-
lems, three SI methods are implemented: a PSO, an ABC, and an electromagnetism
mechanism algorithm. Furthermore, in order to enhance their performance, all these
three methods are hybridized with a variable neighborhood search algorithm. In
[123], Yao et al. present a PSO for solving a logistic problem focused on collaborative
pickup of the cartons from several factories to a collection depot, for later serving
them to corresponding clients through the use of a heterogeneous fleet. In [124],
authors introduce an improved version of the BA for solving a real-world medical
goods distribution problem with pharmacological waste collection. The developed
improved technique employs the well-known Hamming Distance for calculating the
difference in the bats comprising the population, in order to adapt to the neigh-
borhoods in which individuals move. This same mechanism is also used in other
transportation-related investigations such as [125].
Interesting is also the work proposed in [126], in which a so-called feeder logistic
problem is addressed by an ACO. In that problem, clients can be served by either a
small (motorcycle) or a large (truck) vehicle. In that problem, both types of vehicles
depart from the depot, serve the customers, and then return to the warehouse. Fur-
thermore, during the delivery process, motorcycles visit the clients and also travel
to the truck along the execution of the route for reloading purposes. In another vein,
in [127], an ACO is also used for dealing with a fresh seafood delivery problem. In
[128], a valuable study is proposed, focused on building optimized routes to minimize
the evacuation times of people walking away from a tsunami. For constructing these
routes, the system uses a SI method. Similar studies can be found in [129] or [130],
in which different methods are proposed for planning walking evacuation routes.
Further examples of SI methods applied to transportation and logistics problems can
be found in [131, 132].
Another interesting research activity can be found around the coined as Multi-
Attribute Traveling Salesman Problems or Multi-Attribute Vehicle Routing Problems
[133]. These kinds of problems are specific cases of both TSP or VRP with multiple
restrictions. The principal features of these problems are their complex formula-
tions, leading to an increased complexity of resolution. These concrete problems are
especially important in the current community since they are usually modeled for
addressing real-world transportation and logistics problems. Some recent examples
can be found in [134, 135].

3.2 Swarm Intelligence in Industry
In recent years, a growing number of works have been published focused on solving
problems arising in industrial settings through the use of SI techniques. The current
momentum gained by this field has led to a wide variety of applications in this specific
field. Being impossible to cover all these fields of application, we focus our attention
on two prolific branches. On the one hand, we revolve around the well-known Job-
Shop Scheduling problem, and how technological advances have impacted its current
research. On the other hand, we turn our attention to a prolific and relatively new
application context, which is strictly related to SI: Swarm Robotics.
Regarding the Job-Shop Scheduling Problem (JSP, [136]), a myriad of work has
been published in recent years, presenting advances in different directions. On the
one hand, many studies have been introduced revolving around the adaptation of
different sophisticated SI approaches to already existing formulations of the JSP.
In [57], for example, Nouiri et al. introduced an effective and distributed PSO for
efficiently dealing with the flexible JSP. The same variant of the JSP is solved in
[137], using in that case a so-called simulations-based CS. Further adaptations of the
CS are explored in [138, 139] for the basic version of the problem. In [140], a parallel
BA is implemented for improving the makespan of the canonical variant of the JSP.
An interesting improved BA is developed in [141]. FA has also been considered many
times in recent years in this specific context, as can be read in works such as [142,
143]. Further investigations on SI methods applied to different well-known variants
of the JSP can be seen in [144].
On the other hand, a significant except of the community has focused on the for-
mulation of new variants of the JSP, aiming at finding reliable solutions to real-world
industrial problems. In [145], for example, a no-idle Permutation JSP is proposed
with the total tardiness criterion minimization, which is tackled by a hybrid technique
combining an estimation of distribution algorithm and CS. Another hybrid method is
introduced in [146] for addressing a flexible JSP under uncertain processing times.
In that case, the study is contextualized in semiconductor manufacturing, and the
method employed combines both genetic operators and a PSO. An interesting vari-
ant of the flexible JSP is unveiled in [147], considering possible machine breakdowns.
In that paper, a PSO is also deemed for the solving of the optimization problem. In
[148], the efficiency of an ACO is explored for the resolution of a highly complex
multi-objective JSP with alternative process plans and unrelated parallel machines.
We recommend [149, 150] for additional examples published in this context.
In another vein, and as introduced before, a specific research trend is emerging
in recent years in the context of the industry: Swarm Robotics (SR, [151]). SR
refers to the application of SI approaches to scenarios in which agents represent
robotic devices. In this way, the main objective of SR is to evaluate how a swarm
of simple robotic tools can communicate, coordinate, and collectively accomplish
diverse complex tasks, which would be impossible to complete through the use of a
single robot. Approaches under the umbrella of this concept have been successfully
applied to a wide variety of real-world situation. Some examples are supervision

missions [152] or agricultural seeding and foraging [153]. Anyway, contexts in which
SR offers better performance are related to exploratory purposes. Some of these
specific tasks regard disaster rescue missions [154], localization of objectives [155],
or scenery mapping problems [156].
Several interesting works have been published in the last few years. Alfeo et al.
[157], for example, deal with the problem of discovering static hidden targets in
not homogeneous environments. To do that, a swarm of small dedicated Unmanned
Aircraft Vehicles is used, implementing a coordination approach hybridizing three
biologically inspired procedures: evolution, flocking, and stigmergy. Additionally,
the problem of landmark detection is solved in [158] employing a swarm comprised
of Autonomous Underwater Vehicles. Furthermore, authors in [159] investigate how
SR could collaboratively fight against the spread of wildfires. Another risky scenario
is addressed in [160], related to different levels of radioactive or chemical leakage
from drums in a nuclear storage facility. Additionally, a BA is implemented in [161]
for the guidance of a swarm in the exploration of closed environments and reaching a
fixed objective. Lastly, an exploratory system that is introduced in [162] proposed the
adoption of the feature of trophallaxis as one of the key ideas behind their efficient
scouting.
For readers interested on works revolving around problems arisen in industrial
environments, we recommend the following studies [163–168].
3.3 Swarm Intelligence in Medicine
The community around medical advances has been benefited from the growing trend
of the SI knowledge field. In the last years, the research conducted in this regard
has been really abundant, being able to give an answer to problems never faced
before. More concretely, a bountiful work has been carried out around the diagnosis
of different anomalies by the adoption of image processing. In [169], for example,
a SI approach based on a PSO is proposed for the automatic detection of solitary
pulmonary nodules, through the analysis of CT images. The same problem is also
dealt in [170], exploring the efficiency of further SI methods. Moreover, the problem
faced in [171] regards the detection of respiratory diseases, like pneumonia and lungs
sarcoidosis, examining x-ray images. In that work, a group of SI solvers is used for
defining the detection systems. The chosen methods are the PSO, FA, ABC, CS, and
ACO. Valuable research is proposed in [172] by Galvez et al. focused on detecting
rational border curves of skin lesions from medical images using a BA. Similar
studies are also presented by the same authors in [83, 173], using in that cases a CS
and FA, respectively. Habib et al. introduced in [174] a comprehensive study on the
multi-objective PSO in feature selection for medical diagnosis, delving into different
aspects such as literature review and applications.
In another vein, image registration is an additional crucial research topic in the
medicine knowledge field. This procedure is employed in a wide variety of medical
applications, such as diagnosis, computed tomography, surgery guidance, or compar-

ison/merge/integration of images from Magnetic Resonance Imaging treatments. In
this situation, the efficient combination of images coming from a single or multiple
patients leads to a valuable normalized frame of reference. Anyway, determining the
optimal features and parameters for an efficient registration is a demanding challenge,
often addressed by the perspective of SI in recent years. In [175], for example, a PSO
is modeled for the optimal registration of medical images, based on their features
and intensity. The same problem is deal in [176] using an improved ACO for its res-
olution. Interesting is also the work proposed in [177], in which a comparative study
of SI algorithms is performed also for this specific topic. In [178], a comprehensive
review is conducted on the PSO applied to multimodal medical image registration.
In [179], the method used for solving this problem is a hybrid Biogeography-based
Optimization algorithm with Elite Learning.
Another application in the area of medicine is completely dependent on the
advances produced in an industry-related topic: robotics. More concretely, the
employment of nano-robots guided by SI techniques for conducting medical proce-
dures is a specific application that is attracting the attention of the related community.
In [180], for example, a nano-robots control strategy is designed for killing malev-
olent cells, using quorum sensing. Moreover, for the guidance of that nano-robots,
a PSO is employed. A similar study is introduced in [181], using also PSO as a
planning algorithm. In [182], an ACO is employed for the guidance of an intelligent
nanonet for the delivery of targeted drugs to concrete malevolent cells. For interested
readers, we recommend related recent studies such as [183, 184].
3.4 Swarm Intelligence in Energy
The last of the application field addressed in this chapter is related to energy. In
recent years, and because of the shortage of fossil-fuel reserves and the demand-
ing environmental regulation, the generation of energy based on renewable sources
has arisen as a promising approach for the near future. In this line, the design and
optimization of efficient energy systems have been a problem often dealt by the
community, having SI methods as a promising alternative for the optimal design of
these systems. In [185], for example, a PSO is proposed for the optimized design
of grid-dependent hybrid photovoltaic-wind energy systems. The same method is
considered in [186] for cost-efficient management in multi-source renewable energy
microgrids. In [187], a PSO is also proposed for the design of hybrid micro-grid
systems through the multi-objective perspective. An interesting study is proposed
in [188] by Basetti and Chandel, focused on the optimal phasor measurement unit
placement for power system observability. The method considered for dealing with
the designed problem is a BA.
A recurrent problem in this application field is the well-known Economic Dispatch
problem (ED), which has the goal to minimize operating costs for all generators
while fulfilling the supply-demand balance and different requisites as active power
generation limits [189]. Regarding SI, significant advances have been performed in

this branch. In [104], for example, a Chaotic BA is proposed for dealing with this
situation. The same metaheuristic is employed in [190] for the tackling of a Non-
Convex ED. Interesting is also the research published in [191], using a hybrid BA
for solving a ED with the consideration of random wind power. In [192], a FA is
modeled for the resolution of an ED in the context of wind thermal power systems.
Also valuable is the work depicted in [193], in which different variants of the FA
are evaluated for the canonical ED. More ambitious is the problem considered in
[194], also tackled by a FA, and which considers dynamism, ramp rate limits, and
line transmission losses. CS metaheuristic has also been frequently used for dealing
with ED, as can be seen in [195, 196]. Further recent examples of SI applied to ED
can be found in [197–199].
Finally, we finish this section highlighting an interesting problem which, in fact,
hybridizes two application fields outlined in this paper: energy optimization and
transportation. In recent years, many advances have been made in this specific niche,
conducting investigations focused on the efficient consumption of energy in trans-
portation systems. One of these problems is related to the efficient usage of electric
vehicles. As can be seen in works such as [200–202], an efficient paradigm has
emerged for dealing with these problems. Another research trend in this framework
is the optimization of fuel consumption of different transportation systems. Again,
many studies have been carried out using SI algorithms for minimizing the consump-
tion of these fossil-fuel resources, as can be seen in [203, 204].
4 Challenges and Opportunities
As has been outlined along this paper, the scientific activity behind SI, optimiza-
tion, and their conjunction for solving real-world problems is really abundant. It is
unquestionablethatthisspecificresearchbranchattractssignificantattentionfromthe
community. This field is in constant evolution, requiring the continuous adaptation
of the community for giving an efficient answer to problems that arise in the cur-
rent society. In this context, the state of the computation and the multiple resources
available open the opportunity of dealing with new challenges. In this regard, we
envisage some research directions through the diverse axis,
• Probably, the first point in which we should pause at regards is the great amount
of SI algorithms that coexist in the literature. Despite the existence of a myriad
of high-performing and reputed technique, there is an excerpt of the community
which still continues scrutinizing the natural world seeking for new biological
phenomena to mimic. In the current literature, it is not hard to find recent works
introducing new metaheuristics inspired by yet unexplored metaphors, such as
[205, 206], presenting algorithms based on the behavior of butterflies in nature
andtheSpanishplayingstyleinsoccer,respectively.Thisuninterruptedelaboration
of novel solvers contributes to the crowding of an already overcrowded literature,
introducing additional methods which do not suppose a clear step forward for the

community. This trend also augments the skepticism of critical voices. Until date,
several influential works havebrought this problemtothefore[2, 207], questioning
the real value of these novel approaches, which are apparently similar to already
existing ones. With this paper, we call researchers to a reflection around the chal-
lenge of stopping the formulation of additional methods. Instead, we encourage the
related community to elaborate on the adaptation of already existing well-known
methods to a more demanding optimization problem. Another valuable activity
regards the exploration of productive synergies and hybridization of already set-
tled solvers and search mechanisms.
• The second challenge is related to the increasing quality of the computational
resources available by practitioners for dealing with the optimization problems.
This fact supposes an opportunity for tackling larger and more demanding prob-
lems and real-world situations. Up to date, a significant excerpt of the litera-
ture deals with controlled datasets and problem instances. Nevertheless, problems
that emerged in the real-world are usually not controlled, and their magnitude is
medium-large sized. There are even features that are unknown or which evolve
along the time. The consideration and optimization of this real-world environment
supposed a challenge for any solving approach. In fact, large instances not only
endanger the efficiency of classical and advanced solving methods, but also jeop-
ardize the convergence of these techniques. For dealing with this situation, we
encourage the consideration of techniques framed in what is coined as large-scale
global optimization. Some examples of these solvers are SHADEILS [208] or
Multiple Offspring Sampling [209], which can unveil exceptional benefits.
• Related to the previous two points, we call researchers through this third challenge
to considering alternative solving schemes and philosophies for dealing with real-
world optimization problems. The deeming of alternative strategies could lead to
the efficiently solving of demanding optimization problems and instances. Some
examples of these strategies are the self-adaptive solvers [210] or cooperative co-
evolutionary algorithms [211]. An additional paradigm that is gaining significant
momentum in the current literature is known as Transfer Optimization [212].
One interesting characteristic of optimization problems is that they do not usually
appear in an isolated way. For this reason, Transfer Optimization explores the use
of what has been learned while solving some previous tasks when dealing with
subsequent tasks or problems. Another promising knowledge branch is coined as
Federated Optimization [213]. In this case, different actors can share their obtained
information with other agents aiming at obtaining diverse advantages.
• Lastly, optimization problems are usually used by researchers for benchmarking
purposes, using mainly the classical variants of the problems, which are arguably
not possible for being applied to real-world situations. On this regard, authors
should turn their attention to the adoption of a concept coined as multi-attribute
or rich problems. These kinds of instances, characterized by having complex for-
mulations and multiple restrictions, are attracting the attention of the community
because of their fidelity with realistic situations. Anyway, the activity behind this
concept is still scarce in comparison to investigations using classic problem formu-
lations for benchmarking purposes. We want with this last challenge to highlight

the need of modeling a new complex formulation of optimization problems, which
can easily be adapted in future stages to complex real-world situations.
5 Conclusions
This paper has been focused on outlining in a systematic way the current state of
the art regarding Swarm Intelligence and its application to optimization problems.
First, we have briefly discussed the recent history of Swarm Intelligence, highlighting
some of the most influential works published in the last years around the most used
SI metaheuristics: Particle Swarm Optimization, Ant Colony Optimization, Artificial
Bee Colony, Firefly Algorithm, Cuckoo Search, and Bat Algorithm. After that, we
have dedicated a section to spotlight some of the most remarkable recent studies on
four of the most intensively studied applied optimization fields: transportation and
logistics, industry, medicine, and energy. Finally, we have concluded this research
by sharing several inspiring challenges and opportunities in this field, hoping to
encourage readers to consider them in the studies carried out in the upcoming years.
Among these challenges, we advocate the deeming of alternative solving approaches,
the facing of larger and more applicable instances, or the exploration of possible
compatibilities between existing solvers and search mechanisms.
Acknowledgements Eneko Osaba would like to thank the Basque Government for its funding
support through the EMAITEK and ELKARTEK (Elkarbot project) programs.
References
1. Kennedy J (2006) Swarm intelligence. In: Handbook of nature-inspired and innovative com-
puting. Springer, pp. 187–219
2. Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN,
Coello CAC, Herrera F (2019) Bio-inspired computation: where we stand and what’s next.
Swarm Evolut. Comput. 48:220–250
3. Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and
bio-inspired computation: theory and applications. Newnes
4. Kennedy J (2010) Particle swarm optimization. In: Encyclopedia of machine learning, pp
760–766
5. Dorigo M. Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings
of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE,
pp 1470–1477
6. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution
7. Schwefel HPP (1993) Evolution and optimum seeking: the sixth generation. Wiley
8. Rechenberg I (1973) Evolution strategy: optimization of technical systems by means of bio-
logical evolution. Fromman-Holzboog Stuttg 104:15–16
9. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with
applications to biology, control, and artificial intelligence
10. Ertenlice O, Kalayci CB (2018) A survey of swarm intelligence for portfolio optimization:
algorithms and applications. Swarm Evolut Comput 39:36–52

11. Yuan S, Wang S, Tian N (2009) Swarm intelligence optimization and its application in geo-
physical data inversion. Appl Geophys 6(2):166–174
12. Del Ser J, Osaba E, Sanchez-Medina JJ, Fister I (2019) Bioinspired computational intelligence
and transportation systems: a long road ahead. IEEE Trans Intell Transp Syst
13. Brezočnik L, Fister I, Podgorelec V (2018) Swarm intelligence algorithms for feature selec-
tion: a review. Appl Sci 8(9):1521
14. Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic opti-
mization: algorithms and applications. Swarm Evolut Comput 33:1–17
15. Yang F, Wang P, Zhang Y, Zheng L, Lu J (2017) Survey of swarm intelligence optimization
algorithms. In: 2017 IEEE international conference on unmanned systems (ICUS). IEEE, pp
544–549
16. Parpinelli RS, Lopes HS (2011) New inspirations in swarm intelligence: a survey. Int J Bio-
Insp Comput 3(1):1–16
17. Yang XS (2014) Swarm intelligence based algorithms: a critical analysis. Evolut intell
7(1):17–28
18. Birbil Şİ, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J
Glob Optim 25(3):263–282
19. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf
Sci 222:175–184
20. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Str
112:283–294
21. Rbouh I, El Imrani AA (2014) Hurricane-based optimization algorithm. AASRI Procedia
6:26–33
22. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf
Sci 179(13):2232–2248
23. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm-a novel
metaheuristic optimization method for solving constrained engineering optimization prob-
lems. Comput Str 110:151–166
24. Salcedo-SanzS(2016)Modernmeta-heuristicsbasedonnonlinearphysicsprocesses:areview
of models and design procedures. Phys Rep 655:1–70
25. Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: World congress on nature &
biologically inspired computing. IEEE, pp 210–214
26. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function
optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471
27. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J
Bio-Insp Comput 2(2):78–84
28. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative
strategies for optimization (NICSO 2010). Springer, pp 65–74
29. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
30. Salcedo-Sanz S (2017) A review on the coral reefs optimization algorithm: new development
lines and current applications. Progress Artif Intell 6(1):1–15
31. Martín A, Vargas VM, Gutiérrez PA, Camacho D, Hervás-Martínez C (2020) Optimising
convolutional neural networks using a hybrid statistically-driven coral reef optimisation algo-
rithm. Appl Soft Comput 90:106144
32. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
33. Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Pacific Rim international
conference on artificial intelligence. Springer, pp 854–858
34. Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for
numerical optimization. Memet Comput 6(1):31–47
35. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control.
IEEE Control Syst 22(3):52–67
36. Yang XS (2012) Flower pollination algorithm for global optimization. In: International con-
ference on unconventional computing and natural computation. Springer, pp 240–249

37. Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–
713
38. Cortés P, García JM, Onieva L, Muñuzuri J, Guadix J (2008) Viral system to solve optimiza-
tion problems: An immune-inspired computational intelligence approach. In: International
Conference on artificial immune systems. Springer, pp 83–94
39. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for
optimization inspired by imperialistic competition. In: IEEE congress on evolutionary com-
putation, (CEC). IEEE, pp 4661–4667
40. Borji A, Hamidi M (2009) A new approach to global optimization motivated by parliamentary
political competitions. Int J Innov Comput Inf Control 5(6):1643–1653
41. Huan TT, Kulkarni AJ, Kanesan J, Huang CJ, Abraham A (2017) Ideology algorithm: a
socio-inspired optimization methodology. Neural Comput Appl 28(1):845–876
42. Ahmadi-Javid A (2011) Anarchic society optimization: a human-inspired method. In: ieee
congress on evolutionary computation (CEC), IEEE, pp 2586–2592
43. Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the
simulation of social behavior. IEEE Trans Evolut Comput 7(4):386–396
44. Duarte A, Fernández F, Sánchez Á, Sanz A (2004) A hierarchical social metaheuristic for
the max-cut problem. In: European conference on evolutionary computation in combinatorial
optimization. Springer, pp 84–94
45. Jin X, Reynolds RG (1999) Using knowledge-based evolutionary computation to solve non-
linear constraint optimization problems: a cultural algorithm approach. In: Proceedings of the
1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 3. IEEE, pp
1672–1678
46. Osaba E, Díaz F, Carballedo R, Onieva E, Perallos A (2014) Focusing on the golden ball
metaheuristic: an extended study on a wider set of problems. Sci World J
47. Osaba E, Diaz F, Onieva E (2013) A novel meta-heuristic based on soccer concepts to solve
routing problems. In: Proceedings of the 15th annual conference companion on Genetic and
evolutionary computation, pp 1743–1744
48. Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm
inspired by FIFA world cup competitions: theory and its application in PID designing for AVR
system. J Control Autom Electr Syst 27(4):419–440
49. Moosavian N, Roodsari BK et al (2013) Soccer league competition algorithm, a new method
for solving systems of nonlinear equations. Int J Intell Sci 4(01):7
50. Shi Y (2011) Brain storm optimization algorithm. In: International conference in swarm
intelligence. Springer, pp 303–309
51. Yampolskiy RV, El-Barkouky A (2011) Wisdom of artificial crowds algorithm for solving
NP-hard problems. Int J Bio-Insp Comput 3(6):358–369
52. Wang J, Cao Y, Li B, Kim HJ, Lee S (2017) Particle swarm optimization based clustering
algorithm with mobile sink for WSNS. Future Gener Comput Syst 76, pp 452–457
53. Yu H, Tan Y, Zeng J, Sun C, Jin Y (2018) Surrogate-assisted hierarchical particle swarm
optimization. Inf Sci 454:59–72
54. Aydilek IB (2018) A hybrid firefly and particle swarm optimization algorithm for computa-
tionally expensive numerical problems. Appl Soft Comput 66:232–249
55. Kiran MS (2017) Particle swarm optimization with a new update mechanism. Appl Soft
Comput 60:670–678
56. Chen X, Tianfield H, Mei C, Du W, Liu G (2017) Biogeography-based learning particle swarm
optimization. Soft Comput 21(24):7519–7541
57. Nouiri M, Bekrar A, Jemai A, Niar S, Ammari AC (2018) An effective and distributed par-
ticle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf
29(3):603–615
58. Wang ZJ, Zhan ZH, Kwong S, Jin H, Zhang J (2020) Adaptive granularity learning distributed
particle swarm optimization for large-scale optimization. IEEE Trans Cybern
59. Jensi R, Jiji GW (2016) An enhanced particle swarm optimization with levy flight for global
optimization. Appl Soft Comput 43:248–261

60. Piotrowski AP, Napiorkowski JJ (2020) Piotrowska. Population size in particle swarm opti-
mization. Swarm Evolut Comput AE, 100718
61. Ünal AN, Kayakutlu G (2020) Multi-objective particle swarm optimization with random
immigrants. Complex Intell Syst 1–16
62. Dabhi D, Pandya K (2020) Enhanced velocity differential evolutionary particle swarm opti-
mization for optimal scheduling of a distributed energy resources with uncertain scenarios.
IEEE Access 8:27001–27017
63. Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on
hybrid strategies for scheduling problem. IEEE Access 7:20281–20292
64. Uthayakumar J, Metawa N, Shankar K, Lakshmanaprabu S (2020) Financial crisis prediction
model using ant colony optimization. Int J Inf Manage 50:538–556
65. Jovanovic R, Tuba M, Voß S (2019) An efficient ant colony optimization algorithm for the
blocks relocation problem. Euro J Oper Res 274(1):78–90
66. Asghari S, Navimipour NJ (2019) Resource discovery in the peer to peer networks using an
inverted ant colony optimization algorithm. Peer-to-Peer Netw Appl 12(1):129–142
67. Yang Q, Chen WN, Yu Z, Gu T, Li Y, Zhang H, Zhang J (2016) Adaptive multimodal contin-
uous ant colony optimization. IEEE Trans Evolut Comput 21(2):191–205
68. Zhou Y, He F, Hou N, Qiu Y (2018) Parallel ant colony optimization on multi-core SIMD
CPUS. Future Gener Comput Syst 79:473–487
69. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In:
Handbook of metaheuristics. Springer, pp 311–351
70. Gao H, Shi Y, Pun CM, Kwong S (2018) An improved artificial bee colony algorithm with
its application. IEEE Trans Ind Inform 15(4):1853–1865
71. Xue Y, Jiang J, Zhao B, Ma T (2018) A self-adaptive artificial bee colony algorithm based on
global best for global optimization. Soft Comput 22(9):2935–2952
72. Jadon SS, Tiwari R, Sharma H, Bansal JC (2017) Hybrid artificial bee colony algorithm with
differential evolution. Appl Soft Comput 58:11–24
73. Sharma TK, Pant M (2017) Shuffled artificial bee colony algorithm. Soft Comput
21(20):6085–6104
74. Gorkemli B, Karaboga D (2019) A quick semantic artificial bee colony programming
(qsABCP) for symbolic regression. Inf Sci 502:346–362
75. Li X, Yang G (2016) Artificial bee colony algorithm with memory. Appl Soft Comput 41:362–
372
76. Luo J, Liu Q, Yang Y, Li X, Chen MR, Cao W (2017) An artificial bee colony algorithm for
multi-objective optimisation. Appl Soft Comput 50:235–251
77. Dedeturk BK, Akay B (2020) Spam filtering using a logistic regression model trained by an
artificial bee colony algorithm. Appl Soft Comput 106229
78. Li G, Cui L, Fu X, Wen Z, Lu N, Lu J (2017) Artificial bee colony algorithm with gene
recombination for numerical function optimization. Appl Soft Comput 52:146–159
79. Thirugnanasambandam K, Prakash S, Subramanian V, Pothula S, Thirumal V (2019) Rein-
forced cuckoo search algorithm-based multimodal optimization. Appl Intell 49(6):2059–2083
80. Osaba E, Del Ser J, Camacho D, Bilbao MN, Yang XS (2020) Community detection in
networks using bio-inspired optimization: latest developments, new results and perspectives
with a selection of recent meta-heuristics. Appl Soft Comput 87:106010
81. Mareli M, Twala B (2018) An adaptive cuckoo search algorithm for optimisation. Appl Com-
put Inform 14(2):107–115
82. Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo
search method. Inf Process Manage 53(4):764–779
83. Gálvez A, Fister I, Osaba E, Del Ser J, Iglesias A (2019) Cuckoo search algorithm for border
reconstruction of medical images with rational curves. In: International conference on swarm
intelligence. Springer, pp 320–330
84. Wang GG, Gandomi AH, Zhao X, Chu HCE (2016) Hybridizing harmony search algorithm
with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285

85. Cui Z, Sun B, Wang G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to
improve DV-hop performance for cyber-physical systems. J Parall Distrib Comput 103:42–52
86. Yang XS, He XS (2020) Bat algorithm and cuckoo search algorithm. In: Nature-inspired
computation and swarm intelligence. Elsevier, pp 19–34
87. Ouaarab A (2020) Cuckoo search: from continuous to combinatorial. In: Discrete cuckoo
search for combinatorial optimization. Springer, pp 31–41
88. Ouaarab A (2020) DCS applications. In: Discrete cuckoo search for combinatorial optimiza-
tion. Springer, pp 45–70
89. Ouaarab A (2020) Random-key cuckoo search (RKCS) applications. In: Discrete cuckoo
search for combinatorial optimization. Springer, pp 71–86
90. Ouaarab A, Ahiod B, Yang XS (2017) Random key cuckoo search for the quadratic assignment
problem. Trans Mach Learn Artif Intell 5(4)
91. Ouaarab A, Ahiod B, Yang XS (2015) Random-key cuckoo search for the travelling salesman
problem. Soft Comput 19(4):1099–1106
92. Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the
cuckoo search algorithm. Appl Soft Comput 61:1041–1059
93. Sudeeptha J, Nalini C (2019) Hybrid optimization of cuckoo search and differential evolu-
tion algorithm for privacy-preserving data mining. In: International conference on artificial
intelligence, smart grid and smart city applications. Springer, pp 323–331
94. Wang H, Wang W, Sun H, Rahnamayan S (2016) Firefly algorithm with random attraction.
Int J Bio-Inspir Comput 8(1):33–41
95. Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X, Cui Z (2017) Firefly algorithm with
neighborhood attraction. Inf Sci 382:374–387
96. Peng H, Zhu W, Deng C, Wu Z (2020) Enhancing firefly algorithm with courtship learning.
Inf Sci
97. Zhang L, Liu L, Yang XS, Dai Y (2016) A novel hybrid firefly algorithm for global optimiza-
tion. PLoS One 11(9):e0163230
98. He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color
image segmentation. Neurocomputing 240:152–174
99. Gálvez A, Iglesias A, Osaba E, Del Ser J (2020) Parametric learning of associative func-
tional networks through a modified memetic self-adaptive firefly algorithm. In: International
conference on computational science. Springer, pp 566–579
100. Xing HX, Wu H, Chen Y, Wang K (2020) A cooperative interference resource allocation
method based on improved firefly algorithm. Def Technol
101. Tilahun SL, Ngnotchouye JMT, Hamadneh NN (2019) Continuous versions of firefly algo-
rithm: a review. Artif Intell Rev 51(3):445–492
102. Yelghi A, Köse C (2018) A modified firefly algorithm for global minimum optimization. Appl
Soft Comput 62:29–44
103. Chakri A, Khelif R, Benouaret M, Yang XS (2017) New directional bat algorithm for contin-
uous optimization problems. Expert Syst Appl 69:159–175
104. Adarsh B, Raghunathan T, Jayabarathi T, Yang XS (2016) Economic dispatch using chaotic
bat algorithm. Energy 96:666–675
105. Satapathy SC, Raja NSM, Rajinikanth V, Ashour AS, Dey N (2018) Multi-level image thresh-
olding using OTSU and chaotic bat algorithm. Neural Comput Appl 29(12):1285–1307
106. Al-Betar MA, Awadallah MA (2018) Island bat algorithm for optimization. Expert Syst Appl
107:126–145
107. Osaba E, Del Ser J, Yang XS, Iglesias A, Galvez A (2020) Coeba: a coevolutionary bat algo-
rithm for discrete evolutionary multitasking. In: International conference on computational
science, pp 244–256
108. Cai X, Wang H, Cui Z, Cai J, Xue Y, Wang L (2018) Bat algorithm with triangle-flipping
strategy for numerical optimization. Int J Mach Learn Cybern 9(2):199–215
109. Yildizdan G, Baykan ÖK (2020) A novel modified bat algorithm hybridizing by differential
evolution algorithm. Expert Syst Appl 141:112949

110. Liang H, Liu Y, Li F, Shen Y (2018) A multiobjective hybrid bat algorithm for combined
economic/emission dispatch. Int J Electr Power Energy Syst 101:103–115
111. Liu Q, Wu L, Xiao W, Wang F, Zhang L (2018) A novel hybrid bat algorithm for solving
continuous optimization problems. Appl Soft Comput 73:67–82
112. Gan C, Cao WH, Liu KZ, Wu M, Wang FW, Zhang SB (2019) A new hybrid bat algorithm
and its application to the ROP optimization in drilling processes. IEEE Trans Ind Inform
113. Yue X, Zhang H (2020) Modified hybrid bat algorithm with genetic crossover operation and
smart inertia weight for multilevel image segmentation. Appl Soft Comput 90:106157
114. Cui Z, Li F, Zhang W (2019) Bat algorithm with principal component analysis. Int J Mach
Learn Cybern 10(3):603–622
115. Hong WC, Li MW, Geng J, Zhang Y (2019) Novel chaotic bat algorithm for forecasting
complex motion of floating platforms. Appl Math Modell 72:425–443
116. Yang XS (2020) Nature-inspired optimization algorithms: challenges and open problems. J
Comput Sci 101104
117. Chan KY, Dillon T, Chang E, Singh J (2013) Prediction of short-term traffic variables using
intelligent swarm-based neural networks. IEEE Trans Control Syst Technol 21(1):263–274
118. Raza A, Zhong M (2017) Lane-based short-term urban traffic forecasting with GA designed
ANN and LWR models. Transp Res Procedia 25:1430–1443
119. Lopez-Garcia P, Onieva E, Osaba E, Masegosa AD, Perallos A (2016) A hybrid method for
short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE
Trans Intell Transp Syst 17(2):557–569
120. Hu W, Yan L, Liu K, Wang H (2016) A short-term traffic flow forecasting method based on
the hybrid PSO-SVR. Neural Process Lett 43(1):155–172
121. Pan Y, Shi Y (2016) Short-term traffic forecasting based on grey neural network with particle
swarm optimization. In: Proceedings of the world congress on engineering and computer
science, vol 2 (2016)
122. Govindan K, Jafarian A, Nourbakhsh V (2019) Designing a sustainable supply chain network
integrated with vehicle routing: a comparison of hybrid swarm intelligence metaheuristics.
Comput Oper Res 110:220–235
123. Yao B, Yu B, Hu P, Gao J, Zhang M (2016) An improved particle swarm optimization for carton
heterogeneousvehicle routingproblemwitha collectiondepot.AnnOperRes242(2):303–320
124. Osaba E, Yang XS, Fister I Jr, Del Ser J, Lopez-Garcia P, Vazquez-Pardavila AJ (2019) A
discrete and improved bat algorithm for solving a medical goods distribution problem with
pharmacological waste collection. Swarm Evolut Comput 44:273–286
125. Osaba E, Del Ser J, Sadollah A, Bilbao MN, Camacho D (2018) A discrete water cycle
algorithm for solving the symmetric and asymmetric traveling salesman problem. Appl Soft
Comput 71:277–290
126. Huang YH, Blazquez CA, Huang SH, Paredes-Belmar G, Latorre-Nuñez G (2019) Solving the
feeder vehicle routing problem using ant colony optimization. Comput Ind Eng 127:520–535
127. Yao B, Chen C, Song X, Yang X (2019) Fresh seafood delivery routing problem using an
improved ant colony optimization. Ann Oper Res 273(1–2):163–186
128. Forcael E, González V, Orozco F, Vargas S, Pantoja A, Moscoso P (2014) Ant colony optimiza-
tion model for tsunamis evacuation routes. Comput-Aided Civil Infrastr Eng 29(10):723–737
129. Hajjem M, Bouziri H, Talbi EG, Mellouli K (2017) Parallel ant colony optimization for
evacuation planning. In: Proceedings of the genetic and evolutionary computation conference
companion. ACM, pp 51–52
130. Liu M, Zhang F, Ma Y, Pota HR, Shen W (2016) Evacuation path optimization based on
quantum ant colony algorithm. Adv Eng Inform 30(3):259–267
131. Trachanatzi D, Rigakis M, Marinaki M, Marinakis Y (2020) A firefly algorithm for the envi-
ronmental prize-collecting vehicle routing problem. Swarm Evolut Comput 100712
132. Osaba E, Yang XS, Diaz F, Onieva E, Masegosa AD, Perallos A (2017) A discrete firefly
algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system
with recycling policy. Soft Comput 21(18):5295–5308

133. Caceres-Cruz J, Arias P, Guimarans D, Riera D, Juan AA (2015) Rich vehicle routing problem:
survey. ACM Comput Surv (CSUR) 47(2):32
134. Maity S, Roy A, Maiti M (2019) A rough multi-objective genetic algorithm for uncertain
constrained multi-objective solid travelling salesman problem. Granul Comput 4(1):125–142
135. Baldoquin MG, Martinez JA, Díaz-Ramírez J (2020) A unified model framework for the
multi-attribute consistent periodic vehicle routing problem. PLoS One 15(8):e0237014
136. Manne AS (1960) On the job-shop scheduling problem. Oper Res 8(2):219–223
137. Phanden RK, Saharan LK, Erkoyuncu JA (2018) Simulation based cuckoo search optimiza-
tion algorithm for flexible job shop scheduling problem. In: Proceedings of the international
conference on intelligent science and technology, pp 50–55
138. Hu H, Lei W, Gao X, Zhang Y (2018) Job-shop scheduling problem based on improved
cuckoo search algorithm. Int J Simul Modell 17(2):337–346
139. Ouaarab A, Ahiod B, Yang XS, Abbad M (2014) Discrete cuckoo search algorithm for job
shop scheduling problem. In: IEEE international symposium on intelligent control (ISIC).
IEEE, pp 1872–1876
140. Dao TK, Pan TS, Pan JS et al (2018) Parallel bat algorithm for optimizing makespan in job
shop scheduling problems. J Intell Manuf 29(2):451–462
141. Chen X, Zhang B, Gao D (2019) An improved bat algorithm for job shop scheduling problem.
In: 2019 IEEE international conference on mechatronics and automation (ICMA). IEEE, pp
439–443
142. Khadwilard A, Chansombat S, Thepphakorn T, Chainate W, Pongcharoen P (2012) Appli-
cation of firefly algorithm and its parameter setting for job shop scheduling. J Ind Technol
8(1):49–58
143. Karthikeyan S, Asokan P, Nickolas S, Page T (2015) A hybrid discrete firefly algorithm
for solving multi-objective flexible job shop scheduling problems. Int J Bio-Inspir Comput
7(6):386–401
144. Gao K, Cao Z, Zhang L, Chen Z, Han Y, Pan Q (2019) A review on swarm intelligence
and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE/CAA J
Automa Sinica 6(4):904–916
145. Sun Z, Gu X (2017) Hybrid algorithm based on an estimation of distribution algorithm and
cuckoosearchforthenoidlepermutationflowshopschedulingproblemwiththetotaltardiness
criterion minimization. Sustainability 9(6):953
146. Jamrus T, Chien CF, Gen M, Sethanan K (2017) Hybrid particle swarm optimization combined
with genetic operators for flexible job-shop scheduling under uncertain processing time for
semiconductor manufacturing. IEEE Trans Semicond Manuf 31(1):32–41
147. Nouiri M, Bekrar A, Jemai A, Trentesaux D, Ammari AC, Niar S (2017) Two stage particle
swarm optimization to solve the flexible job shop predictive scheduling problem considering
possible machine breakdowns. Comput Ind Eng 112:595–606
148. Zhao B, Gao J, Chen K, Guo K (2018) Two-generation pareto ant colony algorithm for multi-
objective job shop scheduling problem with alternative process plans and unrelated parallel
machines. J Intell Manuf 29(1):93–108
149. Engin O, Güçlü A (2018) A new hybrid ant colony optimization algorithm for solving the
no-wait flow shop scheduling problems. Appl Soft Comput 72:166–176
150. Zhong LC, Qian B, Hu R, Zhang CS (2018) The hybrid shuffle frog leaping algorithm based
on cuckoo search for flow shop scheduling with the consideration of energy consumption. In:
International conference on intelligent computing. Springer, pp 649–658
151. Beni G, From swarm intelligence to swarm robotics. In: International workshop on swarm
robotics. Springer, pp 1–9
152. Lewkowicz MA, Agarwal R, Chakraborty N (2019) Distributed algorithm for selecting leaders
for supervisory robotic swarm control. In: International symposium on multi-robot and multi-
agent systems (MRS). IEEE, pp 112–118
153. Albani D, IJsselmuiden J, Haken R, Trianni V (2017) Monitoring and mapping with robot
swarms for agricultural applications. In: 2017 14th IEEE international conference on advanced
video and signal based surveillance (AVSS), IEEE, pp 1–6

154. Couceiro MS (2017) An overview of swarm robotics for search and rescue applications.
In: Artificial intelligence: concepts, methodologies, tools, and applications. IGI Global, pp
1522–1561
155. de Sá AO, Nedjah N, de Macedo Mourelle L (2016) Distributed efficient localization in swarm
robotic systems using swarm intelligence algorithms. Neurocomputing 172:322–336
156. Carrillo M, Sánchez-Cubillo J, Osaba E, Bilbao MN, Del Ser J (2019) Trophallaxis, low-
power vision sensors and multi-objective heuristics for 3D scene reconstruction using swarm
robotics. In: International conference on the applications of evolutionary computation (Part
of EvoStar). Springer, pp 599–615
157. Alfeo AL, Cimino MG, De Francesco N, Lega M, Vaglini G (2018) Design and simulation of
the emergent behavior of small drones swarming for distributed target localization. J Comput
Sci 29:19–33
158. Leblond I, Tauvry S, Pinto M (2019) Sonar image registration for swarm AUVS navigation:
results from swarms project. J Comput Sci, in press
159. Innocente MS, Grasso P (2019) Self-organising swarms of firefighting drones: harnessing the
power of collective intelligence in decentralised multi-robot systems. J Comput Sci 34:80–101
160. Huang X, Arvin F, West C, Watson S, Lennox B (2019) Exploration in extreme environments
with swarm robotic system. In: 2019 IEEE international conference on mechatronics (ICM),
vol 1. IEEE, pp 193–198
161. Suárez P, Iglesias A (2017) Bat algorithm for coordinated exploration in swarm robotics. In:
International conference on harmony search algorithm. Springer, pp 134–144
162. Carrillo M, Gallardo I, Del Ser J, Osaba E, Sanchez-Cubillo J, Bilbao MN, Gálvez A, Iglesias
A (2018) A bio-inspired approach for collaborative exploration with mobile battery recharging
in swarm robotics. In: International conference on bioinspired methods and their applications.
Springer, pp 75–87
163. Ramirez-Atencia C, Rodriguez-Fernandez V, Camacho D (2020) A revision on multi-
criteria decision making methods for multi-UAV mission planning support. Expert Syst Appl
160:113708
164. Precup RE, David RC (2019) Nature-inspired optimization algorithms for fuzzy controlled
servo systems. Butterworth-Heinemann
165. Zhang X, Zhang X (2017) Shift based adaptive differential evolution for PID controller designs
using swarm intelligence algorithm. Clust Comput 20(1):291–299
166. Precup RE, David RC, Petriu EM (2016) Grey wolf optimizer algorithm-based tuning of fuzzy
control systems with reduced parametric sensitivity. IEEE Trans Ind Electron 64(1):527–534
167. Precup RE, David RC, Petriu EM, Szedlak-Stinean AI, Bojan-Dragos CA (2016) Grey wolf
optimizer-based approach to the tuning of pi-fuzzy controllers with a reduced process para-
metric sensitivity. IFAC-PapersOnLine 49(5):55–60
168. Ramirez-Atencia C, Mostaghim S, Camacho D (2020) skpnsga-ii: knee point based moea
with self-adaptive angle for mission planning problems. arXiv preprint arXiv:2002.08867
169. Nithila EE, Kumar S (2017) Automatic detection of solitary pulmonary nodules using swarm
intelligence optimized neural networks on CT images. Eng sci technol Int J 20(3):1192–1202
170. de Pinho Pinheiro CA, Nedjah N, de Macedo Mourelle L (2020) Detection and classification
of pulmonary nodules using deep learning and swarm intelligence. Multimed Tools Appl
79(21):15437–15465
171. Woźniak M, Połap D (2018) Bio-inspired methods modeled for respiratory disease detection
from medical images. Swarm Evolut Comput 41:69–96
172. Gálvez A, Fister Jr, I, Osaba E, Fister I, Ser JD, Iglesias A (2019) Computing rational border
curves of melanoma and other skin lesions from medical images with bat algorithm. In:
Proceedings of the genetic and evolutionary computation conference companion, pp 1675–
1682
173. Gálvez A, Fister I, Osaba E, Del Ser J, Iglesias A (2019) Hybrid modified firefly algorithm
for border detection of skin lesions in medical imaging. In: IEEE congress on evolutionary
computation (CEC). IEEE, pp 111–118

174. Habib M, Aljarah I, Faris H, Mirjalili S (2020) Multi-objective particle swarm optimiza-
tion: theory, literature review, and application in feature selection for medical diagnosis. In:
Evolutionary machine learning techniques. Springer, pp 175–201
175. Abdel-Basset M, Fakhry AE, El-Henawy I, Qiu T, Sangaiah AK (2017) Feature and intensity
based medical image registration using particle swarm optimization. J Med Syst 41(12):197
176. Lin TX, Chang HH (2016) Medical image registration based on an improved ant colony
optimization algorithm. Int J Pharma Med Biol Sci 5(1):17–22
177. Sarvamangala D, Kulkarni RV (2019) A comparative study of bio-inspired algorithms for
medical image registration. In: Advances in intelligent computing. Springer, pp 27–44
178. Rundo L, Tangherloni A, Militello C, Gilardi MC, Mauri G (2016) Multimodal medical
image registration using particle swarm optimization: a review. In: IEEE symposium series
on computational intelligence (SSCI). IEEE, pp 1–8
179. Chen Y, He F, Li H, Zhang D, Wu Y (2020) A full migration BBO algorithm with enhanced
population quality bounds for multimodal biomedical image registration. Appl Soft Comput
106335
180. Ezzat D, Amin S, Shedeed HA, Tolba MF (2019) A new nano-robots control strategy for
killing cancer cells using quorum sensing technique and directed particle swarm optimiza-
tion algorithm. In: International conference on advanced machine learning technologies and
applications. Springer, pp 218–226
181. Ezzat D, Amin S, Shedeed HA, Tolba MF (2020) Controlling directed particle swarm opti-
mization for delivering nano-robots to cancer cells. In: Joint European-US workshop on
applications of invariance in computer vision. Springer, pp 148–158
182. Lin L, Huang F, Yan H, Liu F, Guo W (2020) Ant-behavior inspired intelligent nanonet for
targeted drug delivery in cancer therapy. IEEE Trans NanoBiosci
183. Ezzat D, Amin S, Shedeed HA, Tolba MF (2020) Directed jaya algorithm for delivering
nano-robots to cancer area. Comput Methods Biomechan Biomed Eng 1–11
184. Shahali S, Rastegar Z (2019) Path optimizing and cell’s deformation in manipulation with
AFM nano-robot using genetic algorithm. In: 2019 7th international conference on robotics
and mechatronics (ICRoM). IEEE, pp 254–258
185. Mohamed MA, Eltamaly AM, Alolah AI (2017) Swarm intelligence-based optimization of
grid-dependent hybrid renewable energy systems. Renew Sustain Energy Rev 77:515–524
186. Keles C, Alagoz BB, Kaygusuz A (2017) Multi-source energy mixing for renewable energy
microgrids by particle swarm optimization. In: International artificial intelligence and data
processing symposium (IDAP). IEEE, pp 1–5
187. Azaza M, Wallin F (2017) Multi objective particle swarm optimization of hybrid micro-grid
system: a case study in sweden. Energy 123:108–118
188. Basetti V, Chandel AK (2017) Optimal PMU placement for power system observability using
taguchi binary bat algorithm. Measurement 95:8–20
189. Li X, Fang L, Lu Z, Zhang J, Zhao H (2017) A line flow granular computing approach for
economic dispatch with line constraints. IEEE Trans Power Syst 32(6):4832–4842
190. Talpur N, Rashid Naseem AA, Ullah A (2019) Enhanced bat algorithm for solving non-
convex economic dispatch problem. In: Recent advances on soft computing and data mining:
proceedings of the fourth international conference on soft computing and data mining (SCDM
2020), Melaka, Malaysia, vol 978. Springer Nature, p 419
191. Liang H, Liu Y, Shen Y, Li F, Man Y (2018) A hybrid bat algorithm for economic dispatch
with random wind power. IEEE Trans Power Syst 33(5):5052–5061
192. Banumalar K, Manikandan B, Mahalingam SS (2017) Economic dispatch problem using
clustered firefly algorithm for wind thermal power system. In: International conference on
computational intelligence, cyber security, and computational models. Springer, pp 37–46
193. Moustafa FS, El-Rafei A, Badra N, Abdelaziz AY (2017) Application and performance com-
parison of variants of the firefly algorithm to the economic load dispatch problem. In: 2017
Third international conference on advances in electrical, electronics, information, communi-
cation and bio-informatics (AEEICB). IEEE, pp 147–151

194. Mostefa H, Mahdad B, Srairi K, Mancer N (2018) Dynamic economic dispatch solution with
firefly algorithm considering ramp rate limit’s and line transmission losses. In: International
conference in artificial intelligence in renewable energetic systems. Springer, pp 497–505
195. Nguyen TT, Vo DN, Dinh BH (2016) Cuckoo search algorithm for combined heat and power
economic dispatch. Int J Electr Power Energy Syst 81:204–214
196. Zhao J, Liu S, Zhou M, Guo X, Qi L (2018) Modified cuckoo search algorithm to solve
economic power dispatch optimization problems. IEEE/CAA J Autom Sinica 5(4):794–806
197. Mohd Zamani MK, Musirin I, Suliman SI, Othman MM, Mohd Kamal MF (2017) Multi-
area economic dispatch performance using swarm intelligence technique considering voltage
stability. Int J Adv Sci Eng Inf Technol 7(1):1–7
198. Gupta GK, Goyal S (2017) Particle swarm intelligence based dynamic economic dispatch
with daily load patterns including valve point effect. In: 2017 3rd international conference on
condition assessment techniques in electrical systems (CATCON). IEEE, pp 83–87
199. Jayabarathi T, Raghunathan T, Adarsh B, Suganthan PN (2016) Economic dispatch using
hybrid grey wolf optimizer. Energy 111:630–641
200. Zhang S, Gajpal Y, Appadoo S, Abdulkader M (2018) Electric vehicle routing problem with
recharging stations for minimizing energy consumption. Int J Prod Econ 203:404–413
201. Smiai O, Bellotti F, Berta R, De Gloria A (2017) Exploring particle swarm optimization to
build a dynamic charging electric vehicle routing algorithm. In: international conference on
applications in electronics pervading industry, environment and society. Springer, pp 127–134
202. Verma OP, Aggarwal D, Patodi T (2016) Opposition and dimensional based modified firefly
algorithm. Expert Syst Appl 44:168–176
203. Li Y, Lim MK, Tseng ML (2019) A green vehicle routing model based on modified particle
swarm optimization for cold chain logistics. Ind Manage Data Syst
204. Salehi Sarbijan M, Behnamian J (2020) Multi-product production routing problem by con-
sideration of outsourcing and carbon emissions: particle swarm optimization. Eng Optim
1–17
205. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global opti-
mization. Soft Comput 23(3):715–734
206. Rashid MFFA (2020) Tiki-taka algorithm: a novel metaheuristic inspired by football playing
style. Engineering Computations
207. Sörensen K (2015) Metaheuristics the metaphor exposed. Int Trans Oper Res 22(1):3–18
208. Molina D, LaTorre A, Herrera F (2018) Shade with iterative local search for large-scale global
optimization. In: IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8
209. LaTorre A, Muelas S, Peña JM (2012) Multiple offspring sampling in large scale global
optimization. In: IEEE congress on evolutionary computation. IEEE, pp 1–8
210. Kramer O (2008) Self-adaptive heuristics for evolutionary computation, vol 147. Springer
211. Ma X, Li X, Zhang Q, Tang K, Liang Z, Xie W, Zhu Z (2018) A survey on cooperative
co-evolutionary algorithms. IEEE Trans Evolut Comput, in press
212. Gupta A, Ong YS, Feng L (2017) Insights on transfer optimization: because experience is the
best teacher. IEEE Trans Emerging Topn Comput Intell 2(1):51–64
213. Konečnỳ J, McMahan HB, Ramage D, Richtárik P (2016) Federated optimization: distributed
machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527

Chapter 2
A Review on Ensemble Methods
and their Applications to Optimization
Problems
Carlos Camacho-Gómez, Sancho Salcedo-Sanz, and David Camacho
1 Introduction to Ensemble Methods for Optimization
Optimization problems play an important role in our daily lives, for example, when
we try to adjust our time to the daily tasks or we do efforts to sort out our sur-
rounding space. Those are simple enough tasks, which can be solved in a short time
(sometimes) and without the need of computational assistance. However, in scien-
tific and corporate sectors, optimization problems are usually characterized by hard
non-linear, highly constrained, black boxed or pointed cloud search spaces, in which
even computational (classical) approaches such as gradient-based methods can not
be applied, or obtain poor solutions.
Meta-heuristic approaches have arisen at the top of the selected tools for solv-
ing these kinds of challenging optimization problems. They are stochastic algorithms
where the optimization variables that represent a solution of the problem are encoded
into a set, commonly known as individual. Many meta-heuristic algorithms deal with
a population of potential solutions to the problem. Thus, some procedures of change
(exploration operators) are applied over this population of individuals (typically
crossover and mutation in the classical evolutionary algorithm) are applied sequen-
tially, in such a way that the search space of the problem is explored. Furthermore, in
each iteration, a procedure to select the most promising individuals to promote them
in the search should be included. It is usually known as exploitation operators. In evo-
C. Camacho-Gómez (B) · D. Camacho
Universidad Politécnica de Madrid, C/Ramiro de Maeztu, 7, Madrid, Spain
e-mail: carlos.camacho@upm.es
D. Camacho
e-mail: david.camacho@upm.es
S. Salcedo-Sanz
Universidad de Alcalá, Escuela Politécnica Superior, Alcalá de Henares, Spain
e-mail: sancho.salcedo@uah.es
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
E. Osaba and X.-S. Yang (eds.), Applied Optimization and Swarm Intelligence,
Springer Tracts in Nature-Inspired Computing,
https://doi.org/10.1007/978-981-16-0662-5_2
25

Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing) by Eneko Osaba (editor), Xin-She Yang (editor) (z-lib.org).pdf

Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing) by Eneko Osaba (editor), Xin-She Yang (editor) (z-lib.org).pdf

Recommended

Recommended

More Related Content

Similar to Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing) by Eneko Osaba (editor), Xin-She Yang (editor) (z-lib.org).pdf

Similar to Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing) by Eneko Osaba (editor), Xin-She Yang (editor) (z-lib.org).pdf (20)

Recently uploaded

Recently uploaded (20)

Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing) by Eneko Osaba (editor), Xin-She Yang (editor) (z-lib.org).pdf