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An improved artificial algae algorithm integrated with differential evolution for job-shop scheduling problem
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AI
DOI：10.1007/s10845-021-01888-8.png)
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Abstract
En 中文
For the past decades, practitioners and researchers have been fascinated by the job-shop scheduling problems (JSSP) and have proposed many pristine meta-heuristic algorithms to solve them. JSSP is an NP-hard problem and a combinatorial optimization problem. This paper proposes a highly efficient and superior performance strategy for the artificial algae algorithm (AAA) integrated with the differential evolution (DE), denoted AAADE, to solve JSSP. The new movement algae colonies using DE operators are introduced to the proposed hybrid artificial algae algorithm and DE (AAADE). To improve AAA's intensification ability, the movement using the DE mutation is implemented into the AAA. In the new hybrid method, the DE crossover can update its position based on both movements (helical and DE movements) to increase randomization. Two categories of problems verify the efficiency and validity of the proposed hybrid algorithm, AAADE, namely, CEC 2014 benchmark functions and different job-shop scheduling problems. The AAADE results are compared with other algorithms in the literature. Hence, comparisons numerical experiments validated and verified the quality of the proposed algorithm. Experimental results validate the effectiveness of the proposed hybrid method in producing excellent solutions that are promising and competitive to the state-of-the-art heuristic-based algorithms reported in the literature in most of the benchmark functions in CEC'14 and JSSP.
Keywords:
Artificial algae algorithm
Bio-inspired algorithm
Differential evolution
Job-shop scheduling problem
Metaheuristics
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