GBUO: “The Good, the Bad, and the Ugly” Optimizer

Givi, Hadi; Dehghani, Mohammad; Montazeri, Zeinab; Ramírez-Mendoza, Ricardo A.; Nouri, Nima

doi:10.3390/app11052042

Cited by 15 publications

(9 citation statements)

References 81 publications

(99 reference statements)

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“…Marine Predators Algorithm (MPA) is another swarm-based optimization algorithm that is designed according to the movement strategies that marine predators use when trapping their prey in the oceans [22]. Some other algorithms in this group are: Doctor and Patients Optimization (DPO) [10], Teaching-Learning-Based Optimization (TLBO) [23], Whale Optimization Algorithm (WOA) [24], Two Stage Algorithm(TSO) [25], Donkey Theorem Optimization (DTO) [26], Group Mean Based Optimizer (GMBO) [27], Cat and Mouse-Based Optimizer (CMBO) [28], Following Optimization Algorithm (FOA) [29], Tunicate Swarm Algorithm (TSA) [30], Good and Bad Groups Based Optimizer (GBGBO) [31], Rat Swarm Optimizer (RSO) [32], Good and Bad and Ugly Optimizer (GBUO) [33], and Seagull Optimization Algorithm (SOA) [34].…”

Section: Introductionmentioning

confidence: 99%

POA: Puzzle Optimization Algorithm

2022

IJIES

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Optimization plays a key role in various disciplines of science in order to achieve the optimal solution among all available solutions. Innovation and contribution of this paper is in introducing a new optimization algorithm called Puzzle Optimization Algorithm (POA) to solve various optimization problems. The main idea in the design of the proposed POA is the mathematical simulation of the process of solving a puzzle as an evolutionary optimizer. The various steps of POA are explained and then its mathematical model is presented. The main advantage and feature of the proposed POA is that it has no control parameters and therefore does not require parameter setting. The performance of the proposed POA is tested on twenty-three different objective functions. Also, in order to analyse the performance of POA in optimization, its results are compared with eight other optimization algorithms. The optimization results indicate the high and acceptable ability of the proposed POA to solve optimization problems. In addition, the simulation results show that the proposed POA is far better and much more competitive than the eight compared optimization algorithms.

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Section: Introductionmentioning

confidence: 99%

POA: Puzzle Optimization Algorithm

2022

IJIES

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“…Simulation of the patient treatment process by the doctor has been used in designing doctor and patient optimizer (DPO) [19]. Some of the other swarm-based optimization algorithms are: Seagull Optimization Algorithm (SOA) [20], Whale Optimization Algorithm (WOA) [21], Firefly Algorithm (FA) [22], Artificial Bee Colony (ABC) [23], Cuckoo Search (CS) [24], Bat-inspired Algorithm (BA) [25], Spotted Hyena Optimizer (SHO) [26], Monkey Search (MS) [27], Artificial Fish-Swarm Algorithm (AFSA) [28], Group Optimization (GO) [29], Dolphin Partner Optimization (DPO) [30], Hunting Search (HS) [31], Coupled Spring Forced Bat Algorithm (SFBA) [32], Teaching-Learning-Based Optimization (TLBO) [33], Grey Wolf Optimizer (GWO) [34], Following Optimization Algorithm (FOA) [35], Moth-Flame Optimization Algorithm (MFO) [36], Grasshopper Optimization Algorithm (GOA) [37], Donkey Theorem Optimization (DTO) [38], Emperor Penguin Optimizer (EPO) [39], Multi Leader Optimizer (MLO) [40], Rat Swarm Optimizer (RSO) [41], and "The Good, the Bad, and the Ugly" Optimizer (GBUO) [42].…”

Section: Introductionmentioning

confidence: 99%

AMBO: All Members-Based Optimizer for Solving Optimization Problems

Zeidabadi¹,

Doumari²,

Dehghani³

et al. 2022

Computers, Materials &Amp; Continua

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There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.

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“…For this reason, the solution provided by the optimization algorithms is called quasi-optimal solution. In other words, a quasi-optimal solution is a solution that, if not equal to the global optimal, must be reasonably close to it [3]. Therefore, in comparing the performance of optimization algorithms with each other, the algorithm that provides a better quasioptimal solution is a better algorithm.…”

Section: Introductionmentioning

confidence: 99%

TIMBO: Three Influential Members Based Optimizer

Zeidabadi¹,

Dehghani²,

Malik³

2021

IJIES

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One of the most important and efficient methods in providing suitable solutions for various optimization problems is population-based optimization algorithms. The main contribution and innovation of this paper is to present a new optimization method called Three Influential Members Based Optimizer (TIMBO) which is used for implementation in solving optimization problems. The main idea in designing the proposed TIMBO is to use three important population members with the titles of best member, worst member, and member as mean population in updating the position of population members of the algorithm in the problem search space. The most important feature and advantage of the TIMBO is that it does not have any control parameters, which means that there is no need to control the parameter in this algorithm. TIMBO has been mathematically modeled for use in solving various optimization problems. The efficiency of the TIMBO is analyzed in order to provide suitable quasi-optimal solutions on a set of twenty-three standard objective functions of different types unimodal, high-dimensional multimodal, and fixed-dimensional. Evaluation of unimodal functions indicates the high exploitation power of the proposed TIMBO and evaluation of multimodal functions indicates the appropriate exploration power of the TIMBO. Also, the results obtained from the TIMBO are compared with the performance of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimization (GWO), Grasshopper Optimisation Algorithm (GOA), Hide Object Game Optimizer (HOGO), and Flow Direction Algorithm (FDA). The results of optimization of standard objective functions indicate the high capability of the TIMBO in providing quasi-optimal solutions suitable for various optimization problems. In addition, analyzing and comparing the performance of the other eight optimization algorithms shows that the TIMBO has a more effective ability to solve optimization problems and is much more competitive.

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GBUO: “The Good, the Bad, and the Ugly” Optimizer

Cited by 15 publications

References 81 publications

POA: Puzzle Optimization Algorithm

POA: Puzzle Optimization Algorithm

AMBO: All Members-Based Optimizer for Solving Optimization Problems

TIMBO: Three Influential Members Based Optimizer

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