Received: April 16, 2020. Revised: July 3, 2020. 286 International Journal of Intelligent Engineering and Systems, Vol.13, No.5, 2020 DOI: 10.22266/ijies2020.1031.26 Darts Game Optimizer: A New Optimization Technique Based on Darts Game Mohammad Dehghani 1 * Zeinab Montazeri 1 Hadi Givi 2 Josep M. Guerrero 3 Gaurav Dhiman 4 1 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran 2 Department of Electrical Engineering, Faculty of Engineering, University of Shahreza, Shahreza 86481-41143, Iran 3 Center for Research on Microgrids (CROM), Department of Energy Technology, Aalborg University, Aalborg, Denmark 4 Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab 147004, India * Corresponding author’s Email: adanbax@gmail.com Abstract: In this paper, a novel game-based optimization technique entitled darts game optimizer (DGO) is proposed. The novelty of this investigation is DGO designing based on simulating the rules of Darts game. The key idea in DGO is to get the most possible points by the players in their throws towards the game board. Simplicity of equations and lack of control parameters are the main features of the proposed algorithm. The ability and quality of DGO performance in optimization is evaluated on twenty-three objective functions, and then is compared with eight other optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Whale Optimization Algorithm (WOA), and Marine Predators Algorithm (MPA). The results of simulation and comparison indicate the superiority and optimal quality of the proposed DGO algorithm over the mentioned algorithms. Keywords: Optimization, Optimizer, Darts game, Darts game optimizer, Game-based algorithm. 1. Introduction 1.1 Motivation There are many optimization problems in different disciplines of science and technology that need to be solved using appropriate optimization methods. Hence, employing an effective optimization algorithm is of great importance for solving such problems. In this regard, optimization algorithms have been applied by scientists in various fields such as energy [1], protection [2], electrical engineering [3-6], energy carriers [7,8], and data mining [9] to achieve the optimal solution. This issue motivates researchers to focus on optimization studies, modification of existing methods, and especially introduction of new optimization methods. 1.2 Background In general, optimization algorithms can be categorized into four groups including physics-based, swarm-based, evolutionary-based, and game-based algorithms. Physics-based algorithms are designed based on simulation and application of existing laws in physics. For example, the spring search algorithm (SSA) is designed using Hawk's law in the weight and spring system. In SSA, the members of the population are a number of weights that are connected to each other by a spring and the optimal answer is provided by reaching the equilibrium point [10, 11]. Some of the other algorithms in this category are Ray Optimization (RO) algorithm [12], Black Hole (BH) algorithm [13], Artificial Chemical Reaction Optimization Algorithm (ACROA) [14], Charged