Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

2014 ◽  
Author(s):  
Abdelhamid Bouzidi ◽  
Mohammed Essaid Riffi
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Cat Swarm Optimization

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 Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 286
Author(s):  
Ali Ahmid ◽  
Thien-My Dao ◽  
Ngan Van Le
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
The Right ◽  
Design Cost ◽  
Laminated Structures

Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17.

Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization

2020 ◽  
Vol 12 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Kenneth Brezinski ◽  
Michael Guevarra ◽  
Ken Ferens
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Population Based ◽  
Convergence Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Benchmark Instances

This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.

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