Novel multiobjective particle swarm optimization based on ranking and cyclic distance strategy

Abstract

To effectively improve the convergence and diversity of the multiobjective particle swarm optimization (MOPSO), we proposed a novel MOPSO based on ranking and cyclic distance (RCDMOPSO) that comprehensively considers the spatial target and congestion information of particles.RCDMOPSO introduced a method namely global proportional ranking (GPR) which differs from nondominated ranking under the Pareto framework, and designed a novel external archive maintenance and the global selection strategies of learning sample by combining GPR with cyclic distance. In this paper, RCDMOPSO together with eight classic and state-of-the-art algorithms were examined on ZDT, UF, and DTLZ series to test functions. The results show that RCDMOPSO is highly competitive in achieving the objectives of both convergence and diversity. RCDMOPSO outperformed other popular algorithms such as MOPSOs and multiobjective genetic algorithms based on comprehensive performance evaluation indicators inverted generational distance and hypervolume, thus supporting that RCDMOPSO is an effective approach to tackle multiobjective optimization problems.

Research on Low-Carbon-Emission Scheduling of Workshop under Uncertainty

“…To verify the superiority of the improved algorithm, MOEA/D, NSGA-II, NSGA-III, and the NSGA-III-ST algorithm are selected to perform simulation experiments on ZDT (ZDT1~ZDT4) and DTLZ (DTLZ1, DTLZ2, and DTLZ7) [37] test functions, which are common function sets. The four algorithms use simulated binary crossover and polynomial variation to achieve iterative population evolution, and the variation probability is 1/k, where k denotes the dimensionality of the corresponding decision variables.…”