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In this paper, a Hybrid Genetic-Immune algorithm (HGIA) is developed to solve the flow-shop scheduling problems. The regular genetic algorithm is applied in the first-stage to rapidly evolve and when the processes are converged up to a pre-defined iteration then the Artificial Immune System (AIS) is introduced to hybridize Genetic Algorithm (GA) in the second stage. Therefore, HGIA continues to search...
This paper proposed self-guided genetic algorithm, which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM), to solve strong NP-hard flowshop scheduling problems with the minimization of makespan. Most EAPM research explicitly used the probabilistic model from the parental distribution, then generated solutions by sampling from the probabilistic...
With the increase in manufacturing complexity, conventional production scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. Therefore, applying efficient algorithm to solve the scheduling problems is essential for reducing the time budget. Genetic algorithms (GAs) is very effective in solving discrete combinatorial problems but they are frequently faced...
Many multiobjective evolutionary algorithms are based Pareto domination, among them NSGA II and SPEA 2 are two very popular ones. MOEA/D is a very recent multiobjective evolutionary algorithm using decomposition. In this paper, we implement MOEA/D for multi-objective flowshop scheduling problems. We study the replacement strategy of neighboring solutions, the determination of the reference point,...
This paper presents a novel memetic genetic algorithm (GA) for the flow shop scheduling problem by combining mutation-based local search with traditional genetic algorithm. The local search is based on the depth-first mutation-based searching process and the depth, i. e., the number of total mutation within each generation is according to the number of jobs to be scheduled. In traditional GA, the...
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