Scheduling with multiple performance measures: the one-machine case
Management Science
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem
Computers and Operations Research
A memetic algorithm for the job-shop with time-lags
Computers and Operations Research
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
Computers and Industrial Engineering
A multi-objective PSO for job-shop scheduling problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
Comprehensive learning particle swarm optimization for reactive power dispatch
Applied Soft Computing
International Journal of Computer Integrated Manufacturing
Modular design of a hybrid genetic algorithm for a flexible job-shop scheduling problem
Knowledge-Based Systems
A new dispatching rule based genetic algorithm for the multi-objective job shop problem
Journal of Heuristics
Computers and Operations Research
A hybrid shifting bottleneck-tabu search heuristic for the job shop total weighted tardiness problem
Computers and Operations Research
Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
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As same with many evolutional algorithms, performance of simple PSO depends on its parameters, and it often suffers the problem of being trapped in local optima so as to cause premature convergence. In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is proposed to improve the ability of particles to explore the global and local optimization solutions, and to reduce the probability of being trapped into the local optima. The correctness of the modification, which incorporated a decline disturbance index, was proved. The key question why the proposed method can reduce the probability of being trapped in local optima was answered. The modification improves the ability of particles to explore the global and local optimization solutions, and reduces the probability of being trapped into the local optima. Theoretical analysis, which is based on stochastic processes, proves that the trajectory of particle is a Markov processes and DDPSO algorithm converges to the global optimal solution with mean square merit. After the exploration based on DDPSO, neighborhood search strategy is used in a local search and an adaptive meta-Lamarckian strategy is employed to dynamically decide which neighborhood should be selected to stress exploitation in each generation. The multi-objective combination problems with DDPSO for finding the pareto front was presented under certain performance index. Simulation results and comparisons with typical algorithms show the effectiveness and robustness of the proposed DDPSO.