symmetry SS Article Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem Yu Qiao 1 , Thi-Kien Dao 2, * , Jeng-Shyang Pan 1,2, *, Shu-Chuan Chu 1 and Trong-The Nguyen 2,3 1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; amberjoe1214@163.com (Y.Q.); scchu0803@gmail.com (S.-C.C.) 2 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350014, China; vnthe@hpu.edu.vn 3 Department of Information Technology, Haiphong University of Manage and Technology, Haiphong 180000, Vietnam * Correspondence: jvnkien@gmail.com (T.-K.D.); jengshyangpan@gmail.com (J.-S.P.) Received: 10 February 2020; Accepted: 7 March 2020; Published: 10 March 2020   Abstract: The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results. Keywords: diversity-team; soccer-league competition algorithm; function optimization; WSN nodes coverage 1. Introduction Optimization is one of the most common problems found in life, e.g., engineering design, business planning, or even military applications. Optimization techniques are used to solve problems intelligently by choosing the optimal solution from a large number of solutions [1]. The meta-heuristic algorithm is viral for solving optimization problems as it is robust and straightforward [2–4], for example, reactive power planning problem in power systems [5], capacitated vehicle routing problem [6], and route planning of part process in flexible manufacturing systems [7]. Meta-heuristic algorithms have developed rapidly over the past few decades [8]. Metaheuristic algorithms are developed by taking inspiration from the natural phenomenon, e.g., physical, biological, or swarms moving as an advance seeks to generate or choose promising approximate solutions [9,10]. Examples of population-based algorithms of popular metaheuristics are as follows: genetic algorithm (GA) [11], firefly algorithms (FA) [12], adaptive differential evolution (JADE) [13], Cat swarm optimization Symmetry 2020, 12, 445; doi:10.3390/sym12030445 www.mdpi.com/journal/symmetry