D2MOPSO swMATH ID: 9958 Software Authors: Noura Al Moubayed; Andrei Petrovski; John McCall Description: 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. Homepage: http://link.springer.com/chapter/10.1007/978-3-642-29124-1_7 Related Software: MOEA/D; jMetal; SPEA2; WBMOAIS; AbYSS; DEMORS; SMPSO Referenced in: 4 Publications all top 5 Referenced by 11 Authors 2 Chen, Jianyong 2 Lin, Qiuzhen 2 Ming, Zhong 1 Al Moubayed, Noura 1 Du, Zhihua 1 Huang, Peizhi 1 Li, Jianqiang 1 Michalak, Krzysztof Piotr 1 Petrovskii, Andrei 1 Yu, Jianping 1 Zhu, Qingling Referenced in 3 Serials 1 Computers & Operations Research 1 European Journal of Operational Research 1 Computational Optimization and Applications Referenced in 1 Field 4 Operations research, mathematical programming (90-XX) Referencing Publications by Year