Simplified binary cat swarm optimization

2020 ◽  
Vol 28 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Hugo Siqueira ◽  
Clodomir Santana ◽  
Mariana Macedo ◽  
Elliackin Figueiredo ◽  
Anuradha Gokhale ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Biological Behavior ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Fish School ◽  
Cat Swarm Optimization ◽  
Bee Colony ◽  
Binary Genetic Algorithm ◽  
Improved Performance

Inspired by the biological behavior of domestic cats, the Cat Swarm Optimization (CSO) is a metaheuristic which has been successfully applied to solve several optimization problems. For binary problems, the Boolean Binary Cat Swarm Optimization (BBCSO) presents consistent performance and differentiates itself from most of the other algorithms by not considering the agents as continuous vectors using transfer and discretization functions. In this paper, we present a simplified version of the BBCSO. This new version, named Simplified Binary CSO (SBCSO) which features a new position update rule for the tracing mode, demonstrates improved performance, and reduced computational cost when compared to previous CSO versions, including the BBCSO. Furthermore, the results of the experiments indicate that SBCSO can outperform other well-known algorithms such as the Improved Binary Fish School Search (IBFSS), the Binary Artificial Bee Colony (BABC), the Binary Genetic Algorithm (BGA), and the Modified Binary Particle Swarm Optimization (MBPSO) in several instances of the One Max, 0/1 Knapsack, Multiple 0/1 Knapsack, SubsetSum problem besides Feature Selection problems for eight datasets.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization

2018 ◽  
pp. 337-363
Author(s):  
Prativa Agarwalla ◽  
Sumitra Mukhopadhyay
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Gene Selection ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
High Accuracy ◽  
Marker Genes ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Bee Colony

Pathway information for cancer detection helps to find co-regulated gene groups whose collective expression is strongly associated with cancer development. In this paper, a collaborative multi-swarm binary particle swarm optimization (MS-BPSO) based gene selection technique is proposed that outperforms to identify the pathway marker genes. We have compared our proposed method with various statistical and pathway based gene selection techniques for different popular cancer datasets as well as a detailed comparative study is illustrated using different meta-heuristic algorithms like binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE), binary coded artificial bee colony (BABC) and genetic algorithm (GA). Experimental results show that the proposed MS-BPSO based method performs significantly better and the improved multi swarm concept generates a good subset of pathway markers which provides more effective insight to the gene-disease association with high accuracy and reliability.

Taguchi-Particle Swarm Optimization for Numerical Optimization

2012 ◽  
pp. 44-59
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

scholarly journals Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm

2020 ◽  
Author(s):  
Mohammed Ajuji ◽  
Aliyu Abubakar ◽  
Datti, Useni Emmanuel
Keyword(s):  
Particle Swarm Optimization ◽  
Time Complexity ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Worst Case ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Nature Inspired Algorithms

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

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 The Performance of Penalty Methods on Tree-Seed Algorithm for Numerical Constrained Optimization Problems

2020 ◽  
Vol 17 (5) ◽  
pp. 799-807 ◽  
Author(s):  
Ahmet Cinar ◽  
Mustafa Kiran
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Unconstrained Optimization Problems ◽  
Handling Methods ◽  
State Of Art ◽  
Tree Seed

The constraints are the most important part of many optimization problems. The metaheuristic algorithms are designed for solving continuous unconstrained optimization problems initially. The constraint handling methods are integrated into these algorithms for solving constrained optimization problems. Penalty approaches are not only the simplest way but also as effective as other constraint handling techniques. In literature, there are many penalty approaches and these are grouped as static, dynamic and adaptive. In this study, we collect them and discuss the key benefits and drawbacks of these techniques. Tree-Seed Algorithm (TSA) is a recently developed metaheuristic algorithm, and in this study, nine different penalty approaches are integrated with the TSA. The performance of these approaches is analyzed on well-known thirteen constrained benchmark functions. The obtained results are compared with state-of-art algorithms like Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). The experimental results and comparisons show that TSA outperformed all of them on these benchmark functions

Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

2017 ◽  
pp. 150-180
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Real Time ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Genetic Search ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.

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