An Improved Discrete Particle Swarm Optimization Based on Cooperative Swarms

Xu, Yiheng; Wang, Qiangwei; Hu, Jinglu

doi:10.1109/wiiat.2008.103

Cited by 12 publications

(2 citation statements)

References 4 publications

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“…The new DPSO improves the existing DPSO by introducing an inheritance scheme, inspired by a genetic algorithm into particles construction. Xu et al (2008) presented an improved DPSO based on cooperative swarms, which partitions the search space into lower dimensional subspaces. The k-means split scheme and regular split scheme are applied to split the solution vector into swarms.…”

Section: The Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A discrete shuffled frog optimization algorithm

2011

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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.

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Section: The Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A discrete shuffled frog optimization algorithm

2011

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“…Thus it has the weakness of solving complex problems, and it is difficult to obtain a more accurate solution in the late evolution. Many scholars proposed some improved algorithms (Yuan et al, 2007;Xu et al, 2008;Lovbjerg, 2001), which improve the search capabilities of the elementary PSO in different aspects.…”

Section: Formulation Of Data Association Problemmentioning

confidence: 99%

New Achievements in Evolutionary Computation

Korošec¹

2010

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Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored. However, even with so much work done, novel research into new techniques and new areas of usage is far from over. This book presents some new theoretical as well as practical aspects of evolutionary computation.The first part of the book is mainly concentrated on evolutionary algorithms and their applications. First, the influence that diversity has on evolutionary algorithms will be described. There is also an insight into how to efficiently solve the constraint-satisfaction problem and how time series can be determined by the use of evolutionary forecasting. Quantum finite-state machines are becoming increasingly more important. Here, an evolutionary-based logic is used for its synthesis. With an ever increasing number of criteria being used to evaluate a solution, this is leading to different multi-objective evolutionary approaches. Such approaches are being applied to control optimization and phylogenetic reconstruction. It is well known that evolutionary-computation approaches are mostly bioinspired. So it is interesting to see how they can return to its origin by solving bio-problems. Here, they are used for predicting membrane protein-protein interactions and are applied to different bioinformatics applications.The second part of the book presents some other well-known evolutionary approaches, like genetic algorithms, genetic programming, estimations of the distribution algorithm, and swarm intelligence. Genetic algorithms are used in Q-learning to develop a compact control table, while flight-control system design is being optimized by genetic programming. A new estimation of the distribution algorithm, using the empirical selection distribution, is being presented and, on the other hand, a classical version is being applied to the video-tracking system problem. The book ends with the recently very popular swarm-intelligence approaches, where they are used in artificial societies, social simulations, and applied to the Chinese traveling-salesman problem. This book will be of great value to undergraduates, graduate students, researchers in computer science, and anyone else with an interest in learning about the latest developments in evolutionary computation.

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