Genetic algorithms for assembly line balancing with various objectives
Computers and Industrial Engineering - Special issue: IE in Korea
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Two-sided assembly line balancing to maximize work relatedness and slackness
Computers and Industrial Engineering
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A discrete particle swarm optimization algorithm for uncapacitated facility location problem
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
A mathematical model and a genetic algorithm for two-sided assembly line balancing
Computers and Operations Research
Computers and Operations Research
2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines
Computers and Industrial Engineering
Balancing of mixed-model two-sided assembly lines
Computers and Industrial Engineering
Multi-objective combinatorial optimisation with coincidence algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
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Particle swarm optimisation (PSO) is an evolutionary metaheuristic inspired by the swarming behaviour observed in flocks of birds. The applications of PSO to solve multi-objective discrete optimisation problems are not widespread. This paper presents a PSO algorithm with negative knowledge (PSONK) to solve multi-objective two-sided mixed-model assembly line balancing problems. Instead of modelling the positions of particles in an absolute manner as in traditional PSO, PSONK employs the knowledge of the relative positions of different particles in generating new solutions. The knowledge of the poor solutions is also utilised to avoid the pairs of adjacent tasks appearing in the poor solutions from being selected as part of new solution strings in the next generation. Much of the effective concept of Pareto optimality is exercised to allow the conflicting objectives to be optimised simultaneously. Experimental results clearly show that PSONK is a competitive and promising algorithm. In addition, when a local search scheme (2-Opt) is embedded into PSONK (called M-PSONK), improved Pareto frontiers (compared to those of PSONK) are attained, but longer computation times are required.