| Summary: | Particle swarm optimization (PSO) is a population based meta-heuristic search technique that has been widely applied to deal with various optimization problems. However, like other stochastic methods, PSO also encounters the problems of entrapment into local optima and premature convergence in solving complex multimodal problems. To tackle these issues, a diversity-guided multi-mutation particle swarm optimizer (abbreviated as DMPSO) is presented in this paper. To start with, the chaos opposition-based learning (OBL) is employed to yield high-quality initial particles to accelerate the convergence speed of DMPSO. Followed by, the self-regulating inertia weight is leveraged to strike a balance between the exploration and exploitation in the search space. After that, three different kinds of mutation strategies (gaussian, cauchy and chaotic mutations) are used to maintain the potential diversity of the whole swarm based on an effective diversity-guided mechanism. In particular, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of the DMPSO. Finally, extensive experiments on a set of well-known unimodal and multimodal benchmark functions demonstrate that DMPSO outperforms most of the other tested PSO variants in terms of both the solution quality and its efficiency.
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