No abstract

Recently, there are a number of particle swarm optimization algorithms (PSOs) proposed for tackling multi-objective optimization problems (MOPs). Most of multi-objective PSOs (MOPSOs) were designed to speed up their convergence, which have been validated when tackling various kinds of MOPs. However, they may face some challenges for tackling some complicated MOPs, such as the UF test problems with complicated Pareto-optimal sets, mainly due to their neglect on the diversity. To solve the above problem, a novel hybrid MOPSO (called HMOPSO-ARA) is suggested in this paper with an adaptive resource allocation strategy, which shows a superior performance over most MOPSOs. Using the decomposition approach in HMOPSO-ARA, MOPs are transferred into a set of subproblems, each of which is accordingly optimized by one particle using a novel velocity update approach with the strengthened search capability. Then, an adaptive resource allocation strategy is employed based on the relevant improvement on the aggregated function, which can reasonably assign the computational resource to the particles according to their performance, so as to accelerate the convergence speed to the true Pareto-optimal front. Moreover, a decomposition-based clonal selection strategy is further used to enhance our performance, where the cloning process is run on the external archive based on the relevant fitness improvement. The experiments validate the superiority of HMOPSO-ARA over four competitive MOPSOs (SMPSO, CMPSO, dMOPSO and AgMOPSO) and four competitive multi-objective evolutionary algorithms (MOEA/D-ARA, MOEA/D-DE MOEA/D-GRA and EF_PD) when tackling thirty-five test problems (DTLZ1-DTLZ9, WFG1-WFG9, UF1-UF10 and F1-F9), in terms of two widely used performance indicators.