Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.
TLDR
This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm, a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence.Abstract:
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.read more
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An Optimized Framework for Energy-Resource Allocation in A Cloud Environment based on the Whale Optimization Algorithm.
Shanky Goyal,Shashi Bhushan,Yogesh Kumar,Abu ul Hassan Sarwar Rana,Muhammad Raheel Bhutta,Muhammad Fazal Ijaz,Youngdoo Son +6 more
TL;DR: In this article, the authors used various optimization algorithms such as particle swarm optimization, cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment.
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Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design
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Modeling an Optimized Approach for Load Balancing in Cloud
Muhammad Junaid,Adnan Sohail,Rao Naveed Bin Rais,Adeel Ahmed,Osman Khalid,Imran Khan,Syed Sajid Hussain,Naveed Ejaz +7 more
TL;DR: A load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM that efficiently distributes the load on VMs is proposed.
References
More filters
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Book
Nature-Inspired Metaheuristic Algorithms
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Journal ArticleDOI
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems
TL;DR: The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature.
Journal ArticleDOI
Butterfly optimization algorithm: a novel approach for global optimization
Sankalap Arora,Satvir Singh +1 more
TL;DR: A new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems and results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.