Particle Swarm Optimization Guided Genetic Algorithm: A Novel Hybrid Optimization Algorithm
نویسندگان
چکیده
Abstract—In real world applications, optimization is an inevitable stage in any engineering design. In recent days the optimization theory is also fused into other sciences which require precision in its final result. This topic sounds like a promising domain for research almost in all areas of science and technology. Perhaps several solution methods are proposed for solving problems that require optimization algorithms, in that also the algorithms inspired by natural selection are dominant among them. This paper proposes a hybrid algorithm that integrates two well established methods, one the genetic algorithm (GA) and the other the particle swarm optimization (PSO) algorithm. Here the GA will be the main optimizer and the PSO will be used to guide the GA to locate optimal solutions quickly and effectively. Several benchmark test problems are solved and the applicability of the proposed hybrid algorithm is well established. KeywordGenetic Algorithm, Particle Swarm Optimization, Soft Computing, Benchmark Problems
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