HOGO: Hide Objects Game Optimization

Dehghani, Mohammad; Montazeri, Zeinab; Saremi, Shahrzad; Dehghani, Ali; Malik, O.P.; Al‐Haddad, Kamal; Guerrero, Josep M.

doi:10.22266/ijies2020.0831.19

Cited by 45 publications

(49 citation statements)

References 35 publications

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“…These objective functions are categorized into three different groups including unimodal [47], multimodal [38], and fixed-dimension multimodal functions [41]. In order to analyze the ability of the MLO to solve these objective functions, the results obtained from the simulation of the proposed optimizer are compared with the results of the other eight existing optimization algorithms including GA [29], PSO [35], TLBO [44], GWO [47], GOA [45], EPO [41], SGO [18], and HOGO [19].…”

Section: Simulation Study and Discussionmentioning

confidence: 99%

“…Optimization algorithms are one of the most widely used methods in solving optimization problems. Optimization algorithms that are designed based on various phenomena and laws of nature by random search in the problem-solving space provide a suitable solution close to the global solution of the problem [19].…”

Section: Problem Definitionmentioning

confidence: 99%

“…Simulation of the performance of football clubs in the football game to achieve the championship in the football league is used to design football game based optimization (FGBO) [13]. Other algorithms in this group include: Orientation Search Algorithm (OSA) [15,16] based on simulation of orientation game, Dice Game Optimizer (DGO) [17] based on simulation of dice game, Shell Game Optimization (SGO) [18], and Hide Objects Game Optimization (HOGO) [19].…”

Section: Introductionmentioning

confidence: 99%

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MLO: Multi Leader Optimizer

Dehghani¹,

Montazeri²,

Dehghani³

et al. 2020

IJIES

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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.

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Section: Simulation Study and Discussionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MLO: Multi Leader Optimizer

Dehghani¹,

Montazeri²,

Dehghani³

et al. 2020

IJIES

Self Cite

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show abstract

“…Game-based optimization algorithms are developed based on simulating the behavior of players and referees in various individual and group games. The behavior of the players and the referee in finding the hidden object in the hide object game is applied in the design of the Hide Objects Game Optimization (HOGO) [24]. HOGO offers more suitable quasi-optimal solutions than similar optimization algorithms, but in solving some optimization problems, quasi-optimal solutions need to be provided closer to the global optimal.…”

Section: Introductionmentioning

confidence: 99%

MLBO: Mixed Leader Based Optimizer for Solving Optimization Problems

Zeidabadi¹,

Doumari²,

Dehghani³

et al. 2021

IJIES

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There are numerous optimization problems in various sciences that need to be solved using the appropriate technique. One of the most widely used techniques for solving optimization problems are population-based optimization algorithms. The innovation and contribution of this paper is to design a new optimizer called Mixed Leader Based Optimizer (MLBO) to solve optimization problems. The main idea in the proposed MLBO is to create a new member as a leader by mixing the best population member and a random member to guide the algorithm population. The main advantage and feature of the proposed MLBO is that it has no control parameters and therefore no need to adjust the parameter. The proposed MLBO algorithm is mathematically formulated to implement in solving various optimization problems. The capability of the proposed optimizer in optimizing and providing appropriate solutions has been tested on a set of twenty-three standard objective functions. These objective functions are selected from three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal in order to analyze different aspects of optimization algorithms. Also, in order to analyze the obtained optimization results, the performance of the MLBO is compared with eight other well-known algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching Learning-Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). The obtained optimization results from the MLBO show the proper performance of the proposed algorithm in solving various optimization problems. On the other hand, comparing the performance of the MLBO with the other eight optimization algorithms indicates the superiority of the proposed optimizer over the compared algorithms.

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“…The number of population update phases in the FGBO is high, and this leads to a longer implementation time of this algorithm in solving optimization problems. Simulation of the game rules and the behavior of players in the hidden-object finding game was used in the design of Hide Objects Game Optimization (HOGO) [19].…”

Section: Introductionmentioning

confidence: 99%

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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HOGO: Hide Objects Game Optimization

Cited by 45 publications

References 35 publications

MLO: Multi Leader Optimizer

MLO: Multi Leader Optimizer

MLBO: Mixed Leader Based Optimizer for Solving Optimization Problems

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

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