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.

/pdf/cat-swarm-optimization-algorithm-a-survey-and-performance-1npsxd60rg.pdf

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.

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.

Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation

Free-recall tasks suggest human memory foraging may follow a heavy-tailed distribution, such as a Levy flight, patch foraging, or area-restricted search - walk procedures that are common in other activities of cognitive agents, such as food foraging in both animals and humans. To date, research merely equates memory foraging with hunting in the physical world based on similarities in statistical structure. The current work supports that memory foraging follows a heavy-tailed distribution by using categories with quantitative distances between each item: countries, which have physical distances, and animals, from which cognitive distances can be derived using a multidimensional scaling (MDS) procedure. Likewise, inter-item lag times follow a heavy-tailed distribution. The current work also demonstrates that inter-item distances and times are positively correlated, suggesting the organization of items in memory may be akin to the organization of a physical landscape. Finally, both studies show that participants' original, heavy-tailed lists of countries and animal names produce shorter overall distances traveled than random selection. Human memory foraging follows the same pattern as foraging in the natural world - perhaps because exposure to ecological settings informs our inner cognitive experience - leading to a processing and retrieval time benefit.

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The trajectory of thought: Heavy-tailed distributions in memory foraging promote efficiency.

The Hybrid Radius Particle Swarm Optimization (HRPSO) algorithm is developed, where the social interaction among the agent particle over the radius of swarm circle topology is applied to avoid premature convergence. For the optimization benchmark functions, HRPSO performs better than Particle Swarm Optimization (PSO) and other existing methods. The HRPSO is applied to solve several real-world optimization problems, including the Resource-Constrained Project Scheduling Problem (RCPSP) and Travelling Salesman Problem (TSP). We have designed and investigated different approaches, such as adaptive mutation, forward backward propagation, and k-means combined with the Radius Particle Swarm Optimization (RPSO) to solve these problems. The efficiency of the proposed method is tested against the existing methods. The results show that the HRPSO gives better optimum results.

Hybrid Radius Particle Swarm Optimization Applications

this research paper aims to do a comparative study of three recent optimization metaheuristic approaches that had been applied to solve the NP-hard optimization problem called the travelling salesman problem. The three recent metaheuristics in study are cuckoo search algorithm, cat swarm optimization algorithm and bat-inspired algorithm.  To compare the performances of these methods, the three metaheuristics are applied to solve some benchmark instances of TSPLIB. The obtained results are collected and the error percentage is calculated. The discussion, will present which method is more efficient to solve the real application based on the travelling salesman problem.

/pdf/comparative-analysis-of-three-metaheuristics-for-solving-the-4kji2898ph.pdf

Safaa Bouzidi

Papers

Comparative analysis of Three Metaheuristics for Solving the Travelling Salesman Problem