Author
Hui Sun
Bio: Hui Sun is an academic researcher from Nanchang Institute of Technology. The author has contributed to research in topics: Firefly algorithm & Population. The author has an hindex of 13, co-authored 43 publications receiving 1504 citations.
Papers
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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.
366 citations
TL;DR: A new FA variant called FA with neighborhood attraction (NaFA) is proposed, where each firefly is attracted by other brighter fireflies selected from a predefined neighborhood rather than those from the entire population.
266 citations
TL;DR: A Gaussian bare-bones DE and its modified version (MGBDE) are proposed which are almost parameter free and indicate that the MGBDE performs significantly better than, or at least comparable to, several state-of-the-art DE variants and some existing bare-bone algorithms.
Abstract: Differential evolution (DE) is a well-known algorithm for global optimization over continuous search spaces. However, choosing the optimal control parameters is a challenging task because they are problem oriented. In order to minimize the effects of the control parameters, a Gaussian bare-bones DE (GBDE) and its modified version (MGBDE) are proposed which are almost parameter free. To verify the performance of our approaches, 30 benchmark functions and two real-world problems are utilized. Conducted experiments indicate that the MGBDE performs significantly better than, or at least comparable to, several state-of-the-art DE variants and some existing bare-bones algorithms.
233 citations
TL;DR: A novel multi-strategy ensemble ABC (MEABC) algorithm, where a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring.
221 citations
TL;DR: A new firefly algorithm called FA with random attraction RaFA, which employs a randomly attracted model, and which outperforms the standard FA and two other improved FAs in terms of solution accuracy and robustness.
Abstract: Firefly algorithm FA is a new meta-heuristic optimisation algorithm, which simulates the social behaviour of fireflies based on the flashing and attraction characteristics of fireflies. The standard FA employs a fully attracted model, in which each firefly is attracted by any other brighter firefly in the swarm. However, the fully attracted model may result in slow convergence rate because of too many attractions. In this paper, we propose a new firefly algorithm called FA with random attraction RaFA, which employs a randomly attracted model. In RaFA, each firefly is attracted by another randomly selected firefly. In order to enhance the global search ability of FA, a concept of Cauchy jump is utilised. Experiments are conducted on a set of well-known benchmark functions. Simulation results show that RaFA outperforms the standard FA and two other improved FAs in terms of solution accuracy and robustness. Compared to the standard FA, RaFA has lower computational time complexity.
150 citations
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TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
Abstract: Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.
1,265 citations
01 Jan 2018
TL;DR: Its origin and background is introduced and the theory analysis of the PSO is carried out, which analyzes its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithms, multi-objective optimization PSO and its engineering applications.
Abstract: Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.
1,091 citations
TL;DR: This survey presented a comprehensive investigation of PSO, including its modifications, extensions, and applications to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology.
Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
836 citations
TL;DR: This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO), which performs well on low-dimensional problems and is promising for solving large-scale problems as well.
566 citations
TL;DR: This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm and presents some potential areas for future study.
Abstract: This paper reviews recent studies on the Particle Swarm Optimization PSO algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.
532 citations