Study on hybrid PS-ACO algorithm

Shuang, Bing; Chen, Jiapin; Li, Zhenbo

doi:10.1007/s10489-009-0179-6

Cited by 84 publications

(44 citation statements)

References 12 publications

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“…Our ACO-PSO3 approach is an adaptation of the PS-ACO algorithm proposed by Shuang et al, [7] to the feature selection problem. The authors [7] presented a new hybrid algorithm integrating the ideas of PSO and ACO algorithms and tested it in the well-known travelling salesman problem.…”

Section: Aco-pso3 Approachmentioning

confidence: 99%

“…The authors [7] presented a new hybrid algorithm integrating the ideas of PSO and ACO algorithms and tested it in the well-known travelling salesman problem. In this new algorithm, the pheromone updating rules of ACO are combined with the local and global search mechanisms of PSO.…”

Section: Aco-pso3 Approachmentioning

confidence: 99%

“…When the performance of the best subset selected has not significantly improved for several generations, the searching process is switched to the PSO algorithm. Our third approach ACO-PSO3 is an adaptation of the hybrid PSO-ACO algorithm proposed by Shuang et al [7] to the feature selection problem.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid ACO-PSO Based Approaches for Feature Selection

Menghour¹,

Souici-Meslati²

2016

IJIES

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Abstract:Feature selection attempts to find the most discriminative information aiming to design an accurate learning system. Feature selection has been the focus of interest for a long time and many works had been done. Recently, the tendency of research in this domain is oriented to the bio-inspired methods. In this paper, we propose hybrid bio-inspired approaches applied to the feature selection problem. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with simple bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization and genetic algorithm. Our experimental results show the efficiency of the proposed approaches in the reduction of selected features number and improvement of classification performance.

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Section: Aco-pso3 Approachmentioning

confidence: 99%

Section: Aco-pso3 Approachmentioning

confidence: 99%

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Hybrid ACO-PSO Based Approaches for Feature Selection

Menghour¹,

Souici-Meslati²

2016

IJIES

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“…PSO (Particle Swarm Optimization) simulates foraging behavior of birds and don't need to do a variation, which makes it superior to GA in parameter optimization. So, PSO is applied to optimize the parameters of ACO [10][11][12][13][14] . In paper [10], ,,    , three parameters of ACO are optimized by PSO, so that parameter values have continuity, random and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In paper [10], ,,    , three parameters of ACO are optimized by PSO, so that parameter values have continuity, random and accuracy. B. Shuang [11] proposed a PS-ACO algorithm to solve TSP (Traveling Salesman Problem), and its convergence performance is better than GA and ACO algorithm. ZHANG Chao [12] proposed an ACO algorithm based on parameters optimization by PSO, a pheromone update method of global asynchronous combined with elite strategy is applied, which reduces iterations and has a fast rate in dealing with robot path planning problem.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Detection based on ACO-PSO Algorithm

Chen¹,

Sun²,

Han³

et al. 2015

ijacsa

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Abstract-This survey focuses on the problem of parameters selection in image edge detection by ant colony optimization (ACO) algorithm. By introducing particle swarm optimization (PSO) algorithm to optimize parameters in ACO algorithm, the fitness function based on connectivity of image edge is proposed to evaluate the quality of parameters in ACO algorithm. And the ACO-PSO algorithm is applied to image edge detection. The simulation results show that the parameters have been optimized and the proposed ACO-PSO algorithm presents better edges than traditional methods.

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Immune algorithm combined with estimation of distribution for traveling salesman problem

Wang

et al. 2016

IEEJ Transactions Elec Engng

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This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA-EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA-EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population-based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA-EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA-EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. (Non-member) received the M.S. degree from the University of Toyama (UT), Toyama, Japan, in 2011. He is currently working toward the Ph.D. degree in the Faculty of Engineering, UT. His research interests include artificial immune algorithms, evolutionary algorithms, and combinatorial optimizations. Yirui Wang (Non-member) received the B.S. degree from Donghua University (DU), Shanghai, China, in 2014. He is currently working toward the M.S. degree at the College of Information Sciences and Technology, DU. His current research interests include computational intelligence, swarm intelligent algorithms, and combinatorial optimizations. Zhe Xu S153IEEJ Trans 11(S1): S142-S154 (2016) Z. XU ET AL.Sheng Li (Non-member) received the B.S. degree from Nanjing

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Study on hybrid PS-ACO algorithm

Cited by 84 publications

References 12 publications

Hybrid ACO-PSO Based Approaches for Feature Selection

Hybrid ACO-PSO Based Approaches for Feature Selection

Image Edge Detection based on ACO-PSO Algorithm

Immune algorithm combined with estimation of distribution for traveling salesman problem

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