Volleyball Premier League Algorithm - Moghdani, Reza; Salimifard, Khodakaram (Applied soft computing, 2018)
•A novel population based metaheuristics for optimization problems is proposed.•The VPL performs very well on 23 well-known test functions.•The proposed algorithm provides very competitive results compared to other well-known metaheuristics. This article proposes a novel metaheuristic algorithm called Volleyball Premier League (VPL) inspired by the competition and interaction among volleyball teams during a season. It also mimics the coaching process during a volleyball match. To solve global optimization problems using the volleyball metaphor, there are terms such as substitution, coaching, and learning, which are captured in the VPL algorithm. The proposed algorithm is benchmarked on 23 well-known test functions, which are categorized into three groups, namely unimodal, multimodal and fixed-dimension multimodal functions. The solutions obtained using the VPL have been compared with other metaheuristic algorithms including Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Harmony Search (HS), Sin Cosine Algorithm (SCA), Soccer League Competition (SLC), and League Championship Algorithm (LCA). In addition, VPL has been used to solve three classical engineering design optimization problems. Results show that VPL algorithm possesses a strong capability to produce superior performance over the other well-known metaheuristic algorithms. The results of the experiments also show that the VPL is effectively applicable to solve problems with complex search space.