D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance. D 2 MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leader’s archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D 2 MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Lin, Qiuzhen; Li, Jianqiang; Du, Zhihua; Chen, Jianyong; Ming, Zhong: A novel multi-objective particle swarm optimization with multiple search strategies (2015)
- Lin, Qiuzhen; Zhu, Qingling; Huang, Peizhi; Chen, Jianyong; Ming, Zhong; Yu, Jianping: A novel hybrid multi-objective immune algorithm with adaptive differential evolution (2015)
- Michalak, Krzysztof: Using an outward selective pressure for improving the search quality of the MOEA/D algorithm (2015)
- Al Moubayed, Noura; Petrovski, Andrei; McCall, John: D$^2$MOPSO: multi-objective particle swarm optimizer based on decomposition and dominance (2012)