Table Of ContentStudies in Computational Intelligence 1069
Taymaz Akan
Ahmed M. Anter
A. Şima Etaner-Uyar
Diego Oliva Editors
Engineering
Applications
of Modern
Metaheuristics
Studies in Computational Intelligence
Volume 1069
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
ments and advances in the various areas of computational intelligence—quickly and
with a high quality. The intent is to cover the theory, applications, and design methods
of computational intelligence, as embedded in the fields of engineering, computer
science, physics and life sciences, as well as the methodologies behind them. The
series contains monographs, lecture notes and edited volumes in computational
intelligence spanning the areas of neural networks, connectionist systems, genetic
algorithms, evolutionary computation, artificial intelligence, cellular automata, self-
organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems.
Of particular value to both the contributors and the readership are the short publica-
tion timeframe and the world-wide distribution, which enable both wide and rapid
dissemination of research output.
This series also publishes Open Access books. A recent example is the book Swan,
Nivel, Kant, Hedges, Atkinson, Steunebrink: The Road to General Intelligence
https://link.springer.com/book/10.1007/978-3-031-08020-3
Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago.
All books published in the series are submitted for consideration in Web of Science.
· ·
Taymaz Akan Ahmed M. Anter
·
A. S¸ima Etaner-Uyar Diego Oliva
Editors
Engineering Applications
of Modern Metaheuristics
Editors
Taymaz Akan Ahmed M. Anter
Department of Medicine Egypt-Japan University of Science
Louisiana State University and Technology (E-JUST)
Health Sciences Center Alexandria, Egypt
Shreveport, USA
Faculty of Computers and Artificial
Department of Software Engineering Intelligence
˙Istanbul Topkapı University Beni-Suef University
Istanbul, Turkey Beni Suef, Egypt
A. S¸ima Etaner-Uyar Diego Oliva
Department of Software Engineering Depto. de Innovación Basada en la
Fatih Sultan Mehmet Vakıf University Información y el Conocimiento
Istanbul, Turkey Universidad de Guadalajara, CUCEI
Guadalajara, Jalisco, Mexico
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN 978-3-031-16831-4 ISBN 978-3-031-16832-1 (eBook)
https://doi.org/10.1007/978-3-031-16832-1
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Switzerland AG 2023
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 Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Contents
Empirical Comparison of Heuristic Optimisation Methods
for Automated Car Setup ........................................... 1
Berna Kiraz, Shahriar Asta, Ender Özcan, Muhammet Köle,
and A. S¸ima Etaner-Uyar
Metaheuristic Algorithms in IoT: Optimized Edge Node
Localization ....................................................... 19
Farzad Kiani and Amir Seyyedabbasi
JAYA Algorithm Versus Differential Evolution: A Comparative
Case Study on Optic Disc Localization in Eye Fundus Images .......... 41
J. Prakash and B. Vinoth Kumar
Minimum Transmission Power Control for the Internet of Things
with Swarm Intelligence Algorithms ................................ 51
Ahmet Cevahir Cinar
An Enhanced Gradient Based Optimized Controller for Load
Frequency Control of a Two Area Automatic Generation Control
System ........................................................... 79
Nabil Anan Orka, Sheikh Samit Muhaimin,
Md. Nazmush Shakib Shahi, and Ashik Ahmed
A Meta-Heuristic Algorithm Based on the Happiness Model ........... 109
Aref Yelghi and Shirmohammad Tavangari
Application of Metaheuristic Techniques for Enhancing
the Financial Profitability of Wind Power Generation Systems ......... 127
Prasun Bhattacharjee, Rabin K. Jana, and Somenath Bhattacharya
Optimization of Demand Response .................................. 149
Altaf Q. H. Badar, Rajeev Arya, and Diego Oliva
v
vi Contents
Fitting Curves of Ruminal Degradation Using a Metaheuristic
Approach ......................................................... 167
Muhammed Milani
Optimizing a Real Case Assembly Line Balancing Problem Using
Various Techniques ................................................ 179
Nima Mirzaei and Mazyar Ghadiri Nejad
Multi-circle Detection Using Multimodal Optimization ................ 193
Aydin Cetin, Somaiyeh Rezai, and Taymaz Akan
Empirical Comparison of Heuristic
Optimisation Methods for Automated
Car Setup
Berna Kiraz, Shahriar Asta, Ender Özcan, Muhammet Köle,
and A. S¸ima Etaner-Uyar
Abstract Tuning a race car to improve its performance by adopting an effective
setup is crucial and an extremely challenging task. The Open Racing Car Simulator,
referred to as TORCS, is a well-known simulator in which a race car requires a con-
figuration of twenty two real-valued parameters for an optimal setup. In this study,
various modern (meta)heuristic techniques, such as, evolutionary algorithms, swarm
intelligence algorithm and selection hyper-heuristics, are evaluated using TORCS
to solve the car setup optimisation problem across a range of tracks. An in-depth
performance comparison and analysis of those techniques on the car setup optimisa-
tion problem are provided with a discussion on their strengths and weaknesses. The
empirical results indicate the success of Covariance Matrix Adaptation Evolutionary
Strategy for the car setup optimisation problem.
· ·
Keywords Evolutionary computation Heuristic algorithms Particle swarm
·
optimization Simulation
A. S¸. Etaner-Uyar worked at the Computer Engineering Department of Istanbul Technical
University when she pursued this study.
B
B. Kiraz ( ) · A. S¸. Etaner-Uyar
Department of Computer Engineering, Fatih Sultan Mehmet Vakif University, Istanbul, Turkey
e-mail: [email protected]
A. S¸. Etaner-Uyar
e-mail: [email protected]
S. Asta · E. Özcan
School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
e-mail: [email protected]
M. Köle
Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 1
T. Akan et al. (eds.), Engineering Applications of Modern Metaheuristics,
Studies in Computational Intelligence 1069,
https://doi.org/10.1007/978-3-031-16832-1_1
2 B.Kirazetal.
1 Introduction
The Open Racing Car Simulator (TORCS) [1] based car setup optimisation prob-
lem deals with finding the best (preferably optimal) configuration for the parameters
that determines performance of a race car. A high quality configuration increases
the performance of the car throughout the race. The car itself together with the
racing environment is simulated in TORCS. The simulator, initially a car racing
game, employs a real-world and powerful physical engine which produces a racing
environment with realistic physical effects such as traction, aerodynamics and fuel
consumption [2]. Various racing tracks with a variety of distinguishing features are
embedded in TORCS. For instance, tracks can differ in length, shape, width or sur-
face properties (rough, dirty and etc.). Consequently, various tracks expose racing
cars to different levels of difficulty. A good parameter configuration for the racing
car is hence a successful setting on a wide range of different tracks. The simulator
also provides a non-graphical mode and several vital interfaces. For instance, while
running an experiment involving evolutionary algorithms, one can use the communi-
cation interface to pass the candidate solution to the simulator for objective function
evaluation. The same interface can also be used to collect the feedback from the
simulator. This type of interaction between the objective function and optimisation
algorithms is vital and is often used when objective function and optimisation algo-
rithm are separated (for another example see [3]). In TORCS, the fuel usage can be
incorporated into the evaluations through the use of a flag. Due to fuel consumption
of the racing car over fitness evaluations, TORCS can be considered as a dynamic
optimisation problem.
Metaheuristics [4, 5] provide guidelines for heuristic optimisation. Existing
approaches for dynamic optimisation problems are mostly a single population based
metaheuristics, in particular evolutionary algorithms [6]. The multi-population based
evolutionary algorithms to solve dynamic optimisation problems have also been
growing in the last years. Some recent studies that comprehensively analyse the chal-
lenging issues for multi-population approaches for dynamic optimisation problems
are presented in [7–9]. Besides population-based methods, there are some single-
solution based approaches for dynamic environments [10, 11] as well.
The car setup optimisation problem can be viewed as a global optimisation prob-
lem to explore the best parameter setting for a race car. In this problem, all decision
variables can be real-valued or transformed to real-valued parameters from the dis-
crete space. Moreover, the problem addressed in this paper is dynamic, where prob-
lem features are time varying. This non-stationary characteristic poses a challenge
as it renders optimal or sub-optimal values discovered for specified time period of
little use in future. In this paper, it is aimed to compare the performance of different
modern (meta)heuristic optimisation techniques including evolutionary algorithms,
swarm intelligence algorithm and selection hyper-heuristics which represent meta-
heuristics that search the heuristic space formed by a predefined set of heuristics [12,
13] for optimising the car setup in TORCS environments.
EmpiricalComparisonofHeuristicOptimisationMethodsforAutomatedCarSetup 3
The rest of the paper is organized as follows: Sect. 2 gives the TORCS based car
setup optimisation problem. Section 3 presents an overview of the metaheuristics
and selection hyper-heuristics considered in this study. The details of experimental
design are presented in Sect. 4, which is followed by the experimental results and
their discussion presented in Sect. 5. Finally, Sect. 6 concludes the paper.
2 The Open Racing Car Simulator (TORCS)
Recently, there has been an growing interest in the field of autonomous car rac-
ing [14]. Some simulation software has been used to simulate a car in a racing envi-
ronment. TORCS is a driving simulator widely used to develop high performance
racing vehicles. TORCS software along with its source is available publicly [15]. It is
a cross-platform simulator running on various operating systems. It imposes two main
research challenges: developing an ‘intelligent’ automated driver and optimising the
setup of a car. Evolutionary algorithms have been mainly used to design automatic
drivers in literature [16–20]. A heuristic-based racer called Ahura for TORCS is
proposed in [16]. Ahura contains five modules each of which has some parameters
to be optimised. Ahura employs covariance matrix adaptation evolutionary strategy
(CMA-ES) for optimisation. In [19, 20], evolutionary algorithms are used to optimise
a fuzzy autonomous driver in TORCS. Besides, different strategies including monte
carlo tree search [21], S-metric selection evolutionary multi-objective optimisation
algorithms (SMS-EMOA) [22], finite state machine [23], and deep reinforcement
learning [24, 25, 25–27] have been proposed in literature.
On the other hand, several approaches including evolutionary algorithms, particle
swarm optimisation, and evolutionary strategies have been applied to the car setup
optimisation problem. A multi-objective evolutionary algorithm was introduced by
Munoz et al. for the problem [28]. This algorithm also won the car setup optimisa-
tion competition held at EvoStar 2010. CMA-ES was proposed in [29]tosolvet he
problem. The performance of CMA-ES was compared with that of a genetic algo-
rithm. The experimental results indicate that CMA-ES performs the best. In [30],
the car setup problem was considered as a real-valued optimisation problem. Then,
different methodologies including a set of heuristics and a selection hyper-heuristics
that mixes the set of heuristics were applied to the problem. Based on the results,
the heuristic with best parameter and the selection hyper-heuristic deliver promising
performance in both dynamic and noisy environments. Cotta et al. [31] compared
different approaches using evolutionary computation techniques participated in the
competitions for car setup optimisation. The results show that multi-objective evo-
lutionary algorithms yield good performance for this problem.
The focus of this study is the latter challenge, i.e. optimising the setup of a car.
The TORCS car setup problem requires optimal setting of five parameters of gear-
box ratio, two parameters of rear and front wing angle, four parameters defining the
brake system, two parameters for the rear and front anti-roll bar, five wheel, and four
suspensions parameters, totalling up to twenty two real-valued parameters defining
