Darts Game Optimizer: A New Optimization Technique Based on Darts Game

Dehghani, Mohammad; Montazeri, Zeinab; Givi, Hadi; Guerrero, Josep M.; Dhiman, Gaurav

doi:10.22266/ijies2020.1031.26

Cited by 101 publications

(84 citation statements)

References 34 publications

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“…The good group consists of a certain number of members with the best values of the objective function, and the bad group consists of a certain number of members with the worst values of the objective function. The selection of these two groups is simulated using Equations ( 3)- (6).…”

Section: Group Mean-based Optimizermentioning

confidence: 99%

“…Optimization algorithms have been developed based on simulations of various processes and phenomena of nature, animal behaviors, plants, laws of physics, and rules of games in which there are signs of optimization and progress [3]. For example, the behavior of ants is simulated in the design of the Ant Colony Algorithm (ACO) [4], the simulation of Hooke's physical law is used in the design of the Spring Search Algorithm (SSA) [5], and the simulation of the rules of darts game is used in the design of the Darts Game Optimizer (DGO) [6]. Optimization algorithms are able to provide a suitable solution to optimization problems based on a random scan of the problem search space.…”

Section: Introductionmentioning

confidence: 99%

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GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

2021

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There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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Section: Group Mean-based Optimizermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

2021

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“…• Football Game-Based Optimization (FGBO) [3], Hide Objects Game Optimization (HOGO) [33], Orientation Search Algorithm (OSA) [34,35], Dice Game Optimizer (DGO) [36], Shell Game Optimization (SGO) [37], Darts Game Optimizer (DGO) [38] Ref. [3] It is designed based on mathematical modeling of football league rules and behaviors of football players and clubs.…”

Section: Game-basedmentioning

confidence: 99%

DM: Dehghani Method for Modifying Optimization Algorithms

et al. 2020

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In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems.

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“…Hook's law simulation in a system of weights and springs has been used in the design of the Spring Search Algorithm (SSA) [2]. simulation of darts game and player behaviour are used in the design of the Darts Game Optimizer (DGO) algorithm [3]. An optimization algorithm first delivers solutions randomly, then in an iteration-based process, those solutions are improved at each iteration.…”

Section: Introductionmentioning

confidence: 99%

Mixed Best Members Based Optimizer for Solving Various Optimization Problems

Doumari¹,

Zeidabadi²,

Dehghani³

et al. 2021

IJIES

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Numerous designed optimization problems in different disciplines of science should be solved using appropriate techniques. population based optimization algorithms are one of the powerful tools in solving optimization problems. The innovation of this paper is to present a new optimization algorithm called Mixed Best Members Based Optimizer (MBMBO) that can be used to solve various optimization problems. The main idea in designing the proposed MBMBO algorithm is to create a mixed member of several top members of the population in order to guide and update the algorithm population. The main feature and advantage of the MBMBO is the lack of control parameters. Therefore, the proposed MBMBO does not need to adjust the parameter. The various steps of the MBMBO are described and then mathematically modeled for implementation in solving optimization problems. The performance of the MBMBO in solving optimization problems is evaluated on a set of twenty-three standard objective functions. These objective functions are of three different types, including seven unimodal objective functions, six high dimensional multi-model objective functions, and ten fixed dimensional multi-model objective functions. The results of evaluation of single-model objective functions indicate the high exploitation power and also the results of evaluation of multi-model objective functions indicate the high exploration power of the proposed MBMBO algorithm. Also, the results obtained from the simulation of the MBMBO are compared with the results of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Hide Objects Game Optimization (HOGO), and Shell Game Optimization (SGO). The results of optimizing the objective functions of unimodal and multi-modal types using MBMBO show the acceptable ability of the proposed algorithm to provide suitable solutions. Comparison of the simulation results shows that the proposed MBMBO is much more competitive than the other eight optimization algorithms.

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Darts Game Optimizer: A New Optimization Technique Based on Darts Game

Cited by 101 publications

References 34 publications

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

DM: Dehghani Method for Modifying Optimization Algorithms

Mixed Best Members Based Optimizer for Solving Various Optimization Problems

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