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Artificial Physics Optimization (APO) Algorithm is a novel population-based stochastic algorithm for solving the unconstrained global problems. This paper first presents a simple mechanism to handle constrained optimization problems with APO. A feasibility-based rule is employed, because this rule can guide the swarm quickly to the feasible region and need not additional penalty parameters. The mass...
Through describing the characteristic of current genetic scheduling algorithm, a modified genetic scheduling algorithm (MGA) is proposed according to multi-objective Flexible Job Shop Scheduling Problem. This algorithm introduces a specific representation to reduce the solving space. It obtains the reasonable individuals by the selected principle and weakest link effect. Based on analyzing the benchmark...
Through analyzing the present genetic operators to solve the job shop scheduling problem, a inversion order number genetic algorithm is proposed. In view of the quality of the inversion order number, this algorithm measures the population diversity by the relative inversion order number. It uses the information provided by the inversion order number of the individual and the offspring is generated...
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