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Journal ArticleDOI

Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery

TLDR
This paper proposes a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL), which can provide a level of performance comparable to that given by other advanced optimization techniques.
Abstract
In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 institute of electrical and electronics engineers congress on evolutionary computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

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Citations
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Journal ArticleDOI

Adaptive Particle Swarm Optimization

TL;DR: An adaptive particle swarm optimization that features better search efficiency than classical particle Swarm optimization (PSO) is presented and can perform a global search over the entire search space with faster convergence speed.
Journal ArticleDOI

Particle Swarm Optimization With an Aging Leader and Challengers

TL;DR: ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO and serves as a challenging mechanism for promoting a suitable leader to lead the swarm.
Journal ArticleDOI

Particle Swarm Optimization: Technique, System and Challenges

TL;DR: The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages ofPSO, the basic variant of PS o, Modification of PSo and applications that have implemented using PSO.
Journal ArticleDOI

Diversity enhanced particle swarm optimization with neighborhood search

TL;DR: A hybrid PSO algorithm is proposed, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities.
Journal ArticleDOI

A Self-Learning Particle Swarm Optimizer for Global Optimization Problems

TL;DR: A novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems, which can enable a particle to choose the optimal strategy according to its own local fitness landscape.
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.
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.
Proceedings ArticleDOI

A new optimizer using particle swarm theory

TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Proceedings ArticleDOI

A modified particle swarm optimizer

TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
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