alexa Multi-objective Particle Swarm Optimization with Gradient Descent Search
ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
Open Access

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Research Article

Multi-objective Particle Swarm Optimization with Gradient Descent Search

Li Ma and Babak Forouraghi*

Department of Computer Science, Saint Joseph’s University, USA

Corresponding Author:
Babak Forouraghi
Computer Science Department
Saint Joseph’s University, Pennsylvania, USA
Tel: 610-660-1167
Fax: 610-660-3082
E-mail: [email protected]

Received date: December 23, 2013; Accepted date: May 10, 2014; Published date: May 20, 2014

Citation: Ma L, Forouraghi B (2014) Multi-objective Particle Swarm Optimization with Gradient Descent Search. Int J Swarm Intel Evol Comput 4:113. doi:

Copyright: © 2014 Ma L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Particle swarm optimization (PSO) has been proven to be a reliable method to deal with many types of optimization problems. Specifically, when solving multi-objective PSO (MOPSO) optimization problems careful attention must be paid to parameter selection and implementation strategy in order to improve the performance of the optimizer. This paper proposes a novel MOPSO with enhanced local search ability. A new parameter-less sharing approach is introduced to estimate the density of particles’ neighborhood in the search space. Initially, the proposed method accurately determines the crowding factor of the solutions; in later stages, it effectively guides the entire swarm to converge closely to the true Pareto front. In addition, the algorithm utilizesthe local search method of gradient descent to better explore the Pareto-optimal region. The algorithm’s performance on several test functions and an engineering design problem is reported and compared with other approaches. The obtained results demonstrate that the proposed algorithm is capable of effectively searching along the Pareto-optimal front and identifying the trade-offsolutions.


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