scholarly journals A Survey on Cat Swarm Optimization Algorithm

2021 ◽  
pp. 22-32
Author(s):  
Rasheed R. Ihsan ◽  
Saman M. Almufti ◽  
Bijar M. S. Ormani ◽  
Renas R. Asaad ◽  
Ridwan B. Marqas
Keyword(s):  
Artificial Intelligence ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimal Time ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Intelligent Behavior ◽  
Optimum Solution

Swarm based optimization algorithms are a collection of intelligent techniques in the field of Artificial Intelligence (AI) were developed for simulating the intelligent behavior of animals. Over the years ago, problems complexity increased in a means that it is very difficult for basic mathematical approaches to obtain an optimum solution in an optimal time, this leads the researchers to develop various algorithms base on the natural behaviors of living beings for solving problems. This paper present a review for Cat Swarm Optimization (CSO), which is a powerful metaheuristic swarm-based optimization algorithm inspired by behaviors of cats in the Nature for solving optimization problems. Since its first appearances in 2006, CSO has been improved and applied in different fields by many researchers. In this review, we majorly focus on the original CSO algorithm and some improved branches of CSO family algorithms. Some examples of utilizing CSO to solve problems in engineering are also reviewed.

scholarly journals Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Wusi Yang ◽  
Li Chen ◽  
Yi Wang ◽  
Maosheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Competition Mechanism ◽  
Multiobjective Particle Swarm Optimization

The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.

scholarly journals Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Aram M. Ahmed ◽  
Tarik A. Rashid ◽  
Soran Ab. M. Saeed
Keyword(s):  
Performance Evaluation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Approach ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Cat Swarm Optimization ◽  
Statistical Approaches ◽  
And Performance

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

scholarly journals Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

2020 ◽  
Author(s):  
Aram M. Ahmed ◽  
Tarik A. Rashid ◽  
Soran AM. Saeed
Keyword(s):  
Performance Evaluation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Approach ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Cat Swarm Optimization ◽  
Statistical Approaches ◽  
And Performance

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

Efficient Particle Swarm Optimization Algorithm Based on Affinity Propagation

2014 ◽  
Vol 571-572 ◽  
pp. 191-195
Author(s):  
Lin Ping Su ◽  
Zhao Wang ◽  
Zheng Guan Huang ◽  
Hao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Affinity Propagation ◽  
Swarm Optimization

Since the 1950s, with the great development of computer technology and bionics, particle swarm optimization (PSO) was raised. The particle swarm optimization mimics the nature biological group behaviors, and has the following advantages compared to classic optimization algorithms: it is a global optimization process and doesn’t depend on the initial state; it can be applied widely without prior knowledge on the optimization problems; the ideas and the implements of PSO are quite simple, the steps are standardization, and it’s very convenient to integrate it with other algorithms; PSO is based on the swarm intelligence theory, and it has very good potential parallelism. Particle swarm optimization has a feature that fitness value is used to exchange information in the population, and guides the population to close the optimal solution. Therefore, a mount of fitness should be calculated in swarm intelligence optimization algorithms in order to find the optimal solution or an approximate one. However, when the calculation of the fitness is quite complex, the time cost of this kind of algorithms will be too large. What’s more, the fitness of optimization problems in the real world is often difficult to calculate. Addressing this problem,Efficient Particle Swarm Optimization Algorithm Based on Affinity Propagation (EAPSO) is proposed in this paper.

scholarly journals Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

2020 ◽  
Author(s):  
Aram M. Ahmed ◽  
Tarik A. Rashid ◽  
Soran AM. Saeed
Keyword(s):  
Performance Evaluation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Approach ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Cat Swarm Optimization ◽  
Statistical Approaches ◽  
And Performance

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

scholarly journals Artificial God Optimization – A Creation

2020 ◽  
Vol 7 (6) ◽  
pp. 01-10
Author(s):  
Satish Gajawada ◽  
Hassan Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Complex Optimization ◽  
Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

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

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

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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