A new ant colony optimization with global exploring capability and rapid convergence

Deng, Xiangyang; Yu, Wenbo; Zhang, Limin

doi:10.1109/wcica.2012.6357946

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…Compared with other algorithms, the algorithm has a great improvement in convergence speed, accuracy and robustness. Deng et al proposed an improved ant colony algorithm for traveling salesman problem [17]. Experiments show that the algorithm has good performance and robustness.…”

Section: Parameter Optimization For Ocsvmmentioning

confidence: 99%

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Wang

Song

et al. 2019

IEEE Access

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This paper tries to solve anomaly detection, a very important issue in ensuring the safe and stable operation of power system. As the proportion of abnormal data in the operation of power system is very small, a one-class support vector machine (OCSVM) is adopted in this paper, which is suitable for classification of unbalanced data. However, the performance of OCSVM is sensitive to its parameters, and an unsuitable choice will decrease the classification accuracy and generalization ability of it. In this paper, particle swarm optimization (PSO) is used to optimize the parameters of OCSVM. The original PSO algorithm converges slowly and easily falls into local optimum. To overcome this issue, this paper proposes an improved PSO algorithm for parameters optimization, in which adaptive speed weighting and adaptive population splitting are introduced to improve the convergence speed of the algorithm and help the algorithm jump out of the local optimal position. Experiments on standard benchmarks and real power system experimental data sets demonstrate the effectiveness of the proposed algorithm.

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Section: Parameter Optimization For Ocsvmmentioning

confidence: 99%

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Wang

Song

et al. 2019

IEEE Access

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“…The swarm intelligence optimization algorithm obtains the optimal solution by iteratively updating the population. Representative swarm intelligence optimization algorithms include particle swarm optimization algorithm (PSO) [1]; genetic algorithm (GA) [2]; ant colony optimization algorithm (ACO) [3]; firefly algorithm (FA) [4]; Bat Algorithm (BA) [5].…”

Section: Introductionmentioning

confidence: 99%

Whale Optimization Algorithm Based on Skew Tent Chaotic Map and Nonlinear Strategy

Gao¹,

Ding²,

Xu³

2021

AJCIS

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To solve the problems that whale optimization algorithm is easy to fall into local optimization and slow convergence speed, and the improved whale optimization algorithm is proposed. Firstly, the algorithm uses Skew Tent chaotic map to initialize the whale population, improve the diversity of the original whale population and make the individual position distribution of whales more uniform; Secondly, the nonlinear convergence factor based on inverse incomplete 𝛤 function is used to balance the global exploration and local development ability of whale algorithm. Through the simulation experiments of 8 benchmark functions, from the perspective of mean square deviation and average value, the convergence speed and optimization accuracy of the improved whale optimization algorithm are significantly higher than those of the traditional whale optimization algorithm.

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“…As an important concept of meta‐heuristic algorithms, swarm intelligence algorithms obtain optimal solutions by iteratively updating the population. Representative swarm intelligence algorithms include particle swarm optimization (PSO) 7 which simulates the predatory behaviors of bird flocks; genetic algorithm (GA), 8 which simulate the natural evolution of Darwin's biological evolution theory and the genetic mechanism of biological evolution; ant colony optimization (ACO) algorithm, 9 which simulates ant foraging behaviors; artificial bee colony, 10 which simulates the bee collecting behavior; firefly algorithm (FA), 11 which simulates the glowing behavior of fireflies; and the bat algorithm, 12 which simulates the bat hunting behavior via ultrasound.…”

Section: Introductionmentioning

confidence: 99%

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Ding

Zhao

2020

Concurrency and Computation

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The whale optimization algorithm (WOA) is a new biological meta-heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth-flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed. INDEX TERMS chaotic inertial weightconvergence factorcube mappingmeta-heuristic algorithmwhale optimization algorithm (WOA) 1 INTRODUCTION Meta-heuristic algorithms have the advantages of simplicity and easy implementation. They can effectively avoid falling into a local optima entrapment. They were developed to quickly solve large-scale and complex problems to obtain satisfactory solutions. 1 These algorithms are widely used in production scheduling, 2,3 signal processing, 4 image processing, 5 and dynamic optimization. 6 The idea of meta-heuristic algorithms originated from various biological mechanisms and physical laws. As an important concept of meta-heuristic algorithms, swarm intelligence algorithms obtain optimal solutions by iteratively updating the population. Representative swarm intelligence algorithms include particle swarm optimization (PSO) 7 which simulates the predatory behaviors of bird flocks; genetic algorithm (GA), 8 which simulate the natural evolution of Darwin's biological evolution theory and the genetic mechanism of biological evolution; ant colony optimization (ACO) algorithm, 9 which simulates ant foraging behaviors; artificial bee colony, 10 which simulates the bee collecting behavior; firefly algorithm (FA), 11 which simulates the glowing behavior of fireflies; and the bat algorithm, 12 which simulates the bat hunting behavior via ultrasound. The whale optimization algorithm (WOA) 13 is a new swarm intelligence algorithm proposed by an Australian scholar Mirjalili, inspired by the social behavior of humpback whales. The WOA determines the global optimum by encircling, searching, and attacking the preys. It has a simple principle, few parameter settings, and strong optimization ability; moreover, it successfully solved 29 mathematical and six structural optimization problems. 13 This algorithm has been successfully applied to tasks such as image segmentation, 14 optimized location, 15 and neural network training. 16 However, like other meta-heuristic optimization algorithms, the traditional WOA...

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A new ant colony optimization with global exploring capability and rapid convergence

Cited by 9 publications

References 4 publications

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Whale Optimization Algorithm Based on Skew Tent Chaotic Map and Nonlinear Strategy

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

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