EMOPSO: A Multi-Objective Particle Swarm Optimizer with Emphasis on Efficiency

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4403)


This paper presents the Efficient Multi-Objective Particle Swarm Optimizer (EMOPSO), which is an improved version of a multi-objective evolutionary algorithm (MOEA) previously proposed by the authors. Throughout the paper, we provide several details of the design process that led us to EMOPSO. The main issues discussed are: the mechanism to maintain a set of well-distributed nondominated solutions, the turbulence operator that avoids premature convergence, the constraint-handling scheme, and the study of parameters that led us to propose a self-adaptation mechanism. The final algorithm is able to produce reasonably good approximations of the Pareto front of problems with up to 30 decision variables, while performing only 2,000 fitness function evaluations. As far as we know, this is the lowest number of evaluations reported so far for any multi-objective particle swarm optimizer. Our results are compared with respect to the NSGA-II in 12 test functions taken from the specialized literature.


Pareto Front Evolutionary Computation Multiobjective Optimization Premature Convergence Adaptive Grid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Universidad Autónoma de Nuevo León, AP 34 - F, Cd. Universitaria, San Nicolás de los Garza, NL 66450Mexico
  2. 2.CINVESTAV-IPN (Evolutionary Computation Group), Depto. de Computación, Av. IPN No 2508, Col. San Pedro Zacatenco, México, D.F., 07360Mexico

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