No abstract

No 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.

No abstract

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.

In this paper, a new bio-inspired metaheuristic algorithm called Zebra Optimization Algorithm (ZOA) is developed; its fundamental inspiration is the behavior of zebras in nature. ZOA simulates the foraging behavior of zebras and their defense strategy against predators' attacks. The ZOA steps are described and then mathematically modeled. ZOA performance in optimization is evaluated on sixty-eight benchmark functions, including unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC2015, and CEC2017. The results obtained from ZOA are compared with the performance of nine well-known algorithms. The simulation results show that ZOA can solve optimization problems by creating a suitable balance between exploration and exploitation and has a superior performance compared to nine competitor algorithms. ZOA's ability to solve real-world problems has been tested on four engineering design problems, namely, tension/compression spring, welded beam, speed reducer, and pressure vessel. The optimization results show that ZOA is an effective optimizer in determining the values of the design variables of these problems compared to the nine competitor algorithms.

Heuristic optimization algorithms are widely used to solve problems in different fields of science. In this paper, a new game based optimization method called football game based optimization (FGBO) is presented which simulates the game of football. The population of FGBO are clubs and the variables of the problem are the players belonging to the clubs. FGBO has four phases: a) league holding, b) player transfer, c) practice, and d) promotion and relegation. The power of FGBO in solving optimization problems has been investigated on several benchmark test functions. The result of FGBO and other algorithm are obtained from implantation of these algorithms on unimodal, multimodal, and fixed-dimension multimodal benchmark test functions. Eight optimization algorithms called Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO) have been used to compare these results. The proposed FGBO algorithm is also used to solve the energy commitment (EC) problem. Based on the simulation studies and obtained results, FGBO has a higher efficiency than a number of other algorithms. The results and data obtained from applying FGBO and other mentioned algorithms on unimodal test functions, multimodal test functions, and energy commitment problem show that FGBO is able to provide better results in comparison with other well-known optimization algorithms.