A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

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

Mathematics ◽

10.3390/math9111190 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1190

Author(s):

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Štěpán Hubálovský

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Random Search ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Main Idea ◽

Population Based ◽

Grey Wolf Optimizer ◽

Whale Optimization

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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Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm

Sensors ◽

10.3390/s21155214 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5214

Author(s):

Mohammad Dehghani ◽

Štěpán Hubálovský ◽

Pavel Trojovský

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Population Based ◽

Grey Wolf Optimizer ◽

Objective Functions ◽

Good Ability ◽

Whale Optimization ◽

Teaching Learning

Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.

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Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194017500127 ◽

2017 ◽

Vol 27 (03) ◽

pp. 343-372 ◽

Cited By ~ 10

Author(s):

Umit Can ◽

Bilal Alatas

Keyword(s):

Association Rules ◽

Optimization Problems ◽

Search Algorithm ◽

Fitness Function ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Search Space ◽

Search Method ◽

Quantitative Association Rules ◽

Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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A Spring Search Algorithm Applied to Engineering Optimization Problems

Applied Sciences ◽

10.3390/app10186173 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6173 ◽

Cited By ~ 3

Author(s):

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Gaurav Dhiman ◽

O. P. Malik ◽

Ruben Morales-Menendez ◽

...

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Engineering Optimization ◽

Hooke’S Law ◽

Grasshopper Optimization Algorithm ◽

Hooke's Law ◽

Engineering Optimization Problems

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.

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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Binary Spring Search Algorithm for Solving Various Optimization Problems

Applied Sciences ◽

10.3390/app11031286 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1286 ◽

Cited By ~ 1

Author(s):

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Ali Dehghani ◽

Om P. Malik ◽

Ruben Morales-Menendez ◽

...

Keyword(s):

Optimization Algorithm ◽

High Performance ◽

Optimization Problems ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Bat Algorithm ◽

Search Space ◽

Binary Particle Swarm Optimization ◽

Dragonfly Algorithm ◽

Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

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Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic

Modern Applied Science ◽

10.5539/mas.v12n1p32 ◽

2017 ◽

Vol 12 (1) ◽

pp. 32 ◽

Cited By ~ 6

Author(s):

Amjad A. Hudaib ◽

Hussam N. Fakhouri

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Optimization Algorithms ◽

Particle Swarm ◽

Grey Wolf Optimizer ◽

Natural Phenomena ◽

Particle Swarm Optimizer ◽

Whale Optimization ◽

Moth Flame Optimization Algorithm ◽

State Of Art

Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by the supernova phenomena in nature. SO mimics this natural phenomena aiming to improve the three main features of optimization; exploration, exploitation, and local minima avoidance. The proposed meta-heuristic optimizer has been tested over 20 will known benchmarks functions, the results have been verified by a comparative study with the state of art optimization algorithms Grey Wolf Optimizer (GWO), A Sine Cosine Algorithm for solving optimization problems (SCA), Multi-Verse Optimizer (MVO), Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (MFO), The Whale Optimization Algorithm (WOA), Polar Particle Swarm Optimizer (PLOARPSO) and with Particle Swarm Optimizer (PSO). The results showed that SO provided very competitive and effective results. It outperforms the best state-of-art algorithms that are compared to on the most of the tested benchmark functions.

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An adaptive gravitational search algorithm for global optimization

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i2.pp724-729 ◽

2019 ◽

Vol 16 (2) ◽

pp. 724

Author(s):

Ying-Ying Koay ◽

Jian-Ding Tan ◽

Chin-Wai Lim ◽

Siaw-Paw Koh ◽

Sieh-Kiong Tiong ◽

...

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Population Based ◽

Adaptive Search ◽

Step Size ◽

Search Mechanism ◽

Gravitational Search

<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

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Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp293-302 ◽

2022 ◽

Vol 12 (1) ◽

pp. 293

Author(s):

Randa Jalaa Yahya ◽

Nizar Hadi Abbas

Keyword(s):

Optimization Algorithm ◽

Sliding Mode ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Grey Wolf Optimizer ◽

Sliding Mode Controller ◽

Grey Wolf ◽

Integral Sliding Mode ◽

Whale Optimization ◽

Nature Inspired Algorithm

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.

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A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems

Scientific Programming ◽

10.1155/2022/8249030 ◽

2022 ◽

Vol 2022 ◽

pp. 1-35

Author(s):

Shaomi Duan ◽

Huilong Luo ◽

Haipeng Liu

Keyword(s):

Optimization Algorithm ◽

Pid Control ◽

Control Parameter ◽

Optimization Problems ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Local Optimum ◽

Engineering Problems ◽

Complex Valued ◽

Seeker Optimization Algorithm

This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.

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