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• W1796315384 abstract "Although the particle swarm optimization (PSO) algorithm has been widely used to solve many real world problems, it is likely to suffer entrapment in local optima when addressing multimodal optimization problems. In this paper, we report a novel hybrid optimization algorithm called crisscross search particle swarm optimization (CSPSO), which is different from PSO and its variants in that each particle is directly represented by pbest. The population of particles in CSPSO is updated by modified PSO as well as crisscross search optimization (CSO) in sequence within each iteration. CSO is incorporated as an evolutionary catalytic agent that has powerful capability of searching for pbests of high quality, therefore accelerating the global convergence of PSO. CSO enhances PSO by two search operators, namely horizontal crossover and vertical crossover. The horizontal crossover further enhances PSO's global convergence ability while the vertical crossover can enhance swarm diversity. Several benchmark functions are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that CSPSO outperforms other state-of-the-art PSO variants significantly in terms of global search ability and convergence speed on almost all of the benchmark functions tested." @default.
• W1796315384 created "2016-06-24" @default.
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• W1796315384 date "2016-02-01" @default.
• W1796315384 modified "2023-10-15" @default.
• W1796315384 title "Accelerating particle swarm optimization using crisscross search" @default.
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• W1796315384 doi "https://doi.org/10.1016/j.ins.2015.08.018" @default.
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