Title :
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
Author :
Liu, Dasheng ; Tan, K.C. ; Goh, C.K. ; Ho, W.K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore
Abstract :
In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution
Keywords :
genetic algorithms; particle swarm optimisation; search problems; convergence; directed local fine-tuning; global search ability; multiobjective memetic algorithm; particle swarm optimization; Birds; Computational modeling; Constraint optimization; Convergence; Design optimization; Evolutionary computation; Genetic algorithms; Particle swarm optimization; Sorting; Stochastic processes; Memetic algorithm (MA); multiobjective (MO) optimization; particle swarm optimization (PSO); Algorithms; Animals; Artificial Intelligence; Behavior, Animal; Biomimetics; Computer Simulation; Models, Biological; Movement; Software; Systems Theory;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2006.883270
