Opposition based PSO and mutation operators

Imran, Muhammad; Jabeen, Hajira; Ahmad, Mubashir; Abbas, Qamar; Bangyal, Waqas Haider

doi:10.1109/icetc.2010.5529629

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…Particles move and store previous behaviours of it and share experiences to store search space. The key merit of POS is its experience to share particle communicate to part or complete swarm to lead motion to detect search space [14]. Each particle will compare the current fitness value with previous optimized results and neighbours in every iteration.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

Devika¹,

Karegowda²

2023

Artificial Intelligence

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In the last few years, bio-inspired optimization techniques have been widely adopted in fields such as computer science, mathematics, and biology in order to optimize solutions. Bio inspired optimization problems are usually nonlinear and restricted to multiple nonlinear constraints to tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This work comprises state-of-art of ten recent bio-inspired algorithms, gap analysis, and its applications namely; Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions on the essence of these algorithms and their connections to self-organization and their applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The original POS later on improved versions of PSO have been proposed by many researchers. Few incremental works of PSO has been discussed in this sub section which support for large scale and multiple optima [14].…”

Section: Pso Conceptmentioning

confidence: 99%

Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

Devika¹,

Karegowda²

2023

Artificial Intelligence

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“…On the other hand, many researchers then suggested and reported good performance by combining PSO with crossover operators (see, e.g., [ 4 , 34 ]) and different mutation strategies, such as Gaussian and Cauchy mutations [ 32 , 93 , 94 , 95 ]. These researches were essentially motivated by the fact that PSO presents difficulty in finding optimal or near-optimal solutions for many complex optimisation problems, including multimodel function optimisation and multi-objective optimisation.…”

Section: Connections To Other Artificial Intelligence Toolsmentioning

confidence: 99%

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Freitas

Lopes

Morgado‐Dias

2020

Entropy

166

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The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.

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“…Although PSO has proved its performance for different practical problems [ 21 ], however, it stuck into local minima [ 22 ]. To overcome this problem, recent research work conducted by the authors has proposed various variants [ 23 – 25 ].…”

Section: Background Studymentioning

confidence: 99%

Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

Imran

Hashim

Elaiza

et al. 2014

The Scientific World Journal

Self Cite

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One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

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Opposition based PSO and mutation operators

Cited by 13 publications

References 12 publications

Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

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