Abstract
Particle swarm optimization (PSO) is a metaheuristic method inspired by the swarming behavior observed in flocks of birds. This paper proposes a novel PSO method, namely the PSO algorithm with negative knowledge (PSONK), to solve the multi-objective mixed-model assembly line balancing (MULB) problem with parallel workstations. PSONK employs the knowledge of relative positions of different particles as opposed to traditional PSO in generating new solution strings. The knowledge about poor solutions is also utilized to avoid the pairs of adjacent tasks appeared in the poor solutions from being selected as parts of the new solution strings in the next generation. The concept of Pareto optimality is employed to allow the conflicting objectives to be optimized simultaneously. Experimental results show clearly that PSONK is a promising algorithm. In addition, PSONK embedded with an appropriate local search (M-PSONK) can result in improved performances.
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