Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution

This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the oper...

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Vydáno v:Applied Mathematical Modelling Ročník 80; s. 929
Hlavní autoři: Elaziz, Mohamed Abd, Li, Lin, Jayasena, K P N, Xiong, Shengwu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier BV 01.04.2020
Témata:
Adaptive algorithms
Algorithms
Archives & records
Big Data
Classification
Evolutionary algorithms
Evolutionary computation
Fireworks
Genetic algorithms
Heuristic methods
Multiple objective analysis
Operators (mathematics)
Optimization
Sorting algorithms
ISSN:1088-8691, 0307-904X
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Shrnutí:This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the operators of the differential evolution algorithm are used as local search operators. In general, the proposed method contains three stages. In the first stage, the population is generated, and the archive is initialized. The second stage updates the solutions using the hybrid salp swarm algorithm and the differential evolution algorithm, and the final stage determines the nondominated solutions and updates the archive. To assess the performance of the proposed approach, a series of experiments were performed. A set of single-objective and multiobjective problems from the 2015 Big Data optimization competition were tested; the dataset contained data with and without noise. The results of our experiments illustrated that the proposed approach outperformed other approaches, including the baseline nondominated sorting genetic algorithm, on all test problems. Moreover, for single-objective problems, the score value of the proposed method was better than that of the traditional multiobjective salp swarm algorithm. When compared with both algorithms, that is, the adaptive DE algorithm with external archive and the hybrid multiobjective firefly algorithm, its score was the largest. In contrast, for the multiobjective functions, the scores of the proposed algorithm were higher than that of the fireworks algorithm framework.
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2019.10.069