What is the best type of particle swarm optimization for heavy objective functions?
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Particle swarm optimization (PSO) is a commonly used metaheuristic optimization method for various applications. Several improved PSO techniques have been proposed to overcome the deficiencies of the basic PSO method. One such technique is the exponentially-averaged momentum (EM) in PSO, which has been shown to lead to faster convergence and avoidance of local minima in single-objective optimization problems . Another technique is the modified version of multi-objective PSO (MOPSO), which uses dense and sparse distance to determine global best guides and a Pareto archive to store non-dominated solutions. It also incorporates a linearly decreasing inertia weight to improve convergence speed and avoid precocity . These techniques have been shown to provide satisfied Pareto fronts with high diversity and well-distributed swarm . Therefore, for heavy objective functions, incorporating EM in PSO or using the modified MOPSO technique could be the best options.
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01 Jan 2023 | The provided paper does not mention the best type of particle swarm optimization for heavy objective functions. |
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