Journal ArticleDOI
A discrete shuffled frog optimization algorithm
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TLDR
A discrete version of the shuffled frog leaping optimization algorithm is presented and it is demonstrated that the proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed.Abstract:
The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.read more
Citations
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Journal ArticleDOI
Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey
TL;DR: The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms and presents several interesting open problems with future research directions for multi- Population optimization methods.
Journal ArticleDOI
PSO-MISMO Modeling Strategy for MultiStep-Ahead Time Series Prediction
Yukun Bao,Tao Xiong,Zhongyi Hu +2 more
TL;DR: This paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons, providing considerable flexibility in model construction.
Journal ArticleDOI
The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review
TL;DR: Top improvements on SFLA are considered for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population.
Journal ArticleDOI
Genetic Algorithm Based Objective Functions Comparative Study for Damage Detection and Localization in Beam Structures
TL;DR: In this article, the authors used the GA for detecting and locating damage in diagnostic beams and formulated the problem as an optimization problem using three objective functions (change of natural frequencies, Modal Assurance Criterion MAC and MAC natural frequency) and showed that the best objective function is based on the natural frequency and MAC while the method of the GA presented its efficiencies in indicating and quantifying multiple damage with great accuracy.
BookDOI
Industrial Applications of Holonic and Multi-Agent Systems
TL;DR: It is argued that multi-agent systems (MAS), as decentralized and intelligent control systems, have an indispensable role to play in enabling the overall resilience of the combined cyber-physical engineering system.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
BookDOI
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
TL;DR: Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.
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