An Efficient Genetic Algorithm(EGA) proposed in this paper was aiming to high-dimensional function optimization. To generate multiple diverse solutions and to strengthen local search ability, the new subspace crossove...
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ISBN:
(纸本)9781479925483
An Efficient Genetic Algorithm(EGA) proposed in this paper was aiming to high-dimensional function optimization. To generate multiple diverse solutions and to strengthen local search ability, the new subspace crossover and timely mutation operators improved by us will be used in EGA. The combination of the new operators allow the integration of randomization and elite solutions analysis to achieve a balance of stability and diversification to further improve the quality of solutions in the case of high-dimensionalfunctions. Standard GA and PRPDPGA proposed already were compared in simulation. Computational studies of benchmark by testing optimizationfunctions suggest that the proposed algorithm was able to quickly achieve good solutions while avoiding being trapped in premature convergence.
The wolf pack algorithm (WPA), a swarm intelligence method inspired by wolf hunting behaviors, faces limitations in convergence accuracy, computational efficiency, and local optima avoidance. This paper proposes a Dis...
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Particle Swarm optimization (PSO) is a population-based search methodology inspired by social behavior observed in nature, such as flocks of birds and schools of fish. In many studies, PSO has been successful in a var...
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ISBN:
(纸本)9788995003848
Particle Swarm optimization (PSO) is a population-based search methodology inspired by social behavior observed in nature, such as flocks of birds and schools of fish. In many studies, PSO has been successful in a variety of optimization problems. The purpose of this paper is to improve performance of the PSO algorithm in case of high-dimensional problems. We propose a novel PSO model, the Rotated Particle Swarm (RPS), which is introduced the coordinate conversion. The numerical simulation results show the RPS is effective in optimizing high-dimensionalfunctions.
To balance exploration and exploitation in sine cosine algorithm (SCA), an improved SCA (ISCA) which combined with opposite-learning and Tent chaos search is introduced. In ISCA, opposite-learning technology is adopte...
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ISBN:
(纸本)9781728101699
To balance exploration and exploitation in sine cosine algorithm (SCA), an improved SCA (ISCA) which combined with opposite-learning and Tent chaos search is introduced. In ISCA, opposite-learning technology is adopted by some better individuals. In ISCA, some inferior individuals of population are regenerated by Tent chaos search technology. Numerical experiments show that ISCA can leap out of the local optimums in a high speed when population maybe trap into a local optimum. The results achieved by ISCA has better performance than comparing algorithms in high dimension benchmark functions.
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