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

• Published in: Applied Mathematical Modelling Vol. 80; pp. 929 - 943
• Main Authors: Elaziz, Mohamed Abd, Li, Lin, Jayasena, K. P. N, Xiong, Shengwu
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.04.2020; Elsevier BV
• Subjects: Adaptive algorithms; Algorithms; Archives & records; Big Data; Big Data optimization problem; Classification; Differential evolution; Evolutionary algorithms; Evolutionary computation; Fireworks; Genetic algorithms; Heuristic methods; Metaheuristic techniques; Multiobjective optimization problem; Multiple objective analysis; Operators (mathematics); Optimization; Salp swarm algorithm; Sorting algorithms; Salp swarm algorithm; Differential evolution; Big Data optimization problem; Multiobjective optimization problem; Metaheuristic techniques
• ISSN: 0307-904X; 1088-8691; 0307-904X
• DOI: 10.1016/j.apm.2019.10.069

•Proposed an alternative method for big data optimization problems.•A hybrid between salp swarm algorithm and differential evolution is used as multi-objective for big data optimization.•A set of single objective and multi-objective problems from the 2015 big data optimization competition is used.•Experimental results show that the proposed method outperforms than other methods. 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.