A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

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Self-adaptive Particle Swarm Optimization Algorithm with Mutation Operation based on K-means

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) ◽

10.2991/iccsee.2013.748 ◽

2013 ◽

Cited By ~ 2

Author(s):

Xue-mei Wang ◽

Yi-zhuo Guo ◽

Gui-jun Liu

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Mutation Operation ◽

Self Adaptive

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Multiobjective generalized extremal optimization algorithm for simulation of daylight illuminants

Journal of Photonics for Energy ◽

10.1117/1.jpe.7.048001 ◽

2017 ◽

Vol 7 (04) ◽

pp. 1

Author(s):

Srividya Ravindra Kumar ◽

Ciji Pearl Kurian ◽

Marcos Eduardo Gomes-Borges

Keyword(s):

Optimization Algorithm ◽

Extremal Optimization ◽

Generalized Extremal Optimization

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A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

Mathematical Problems in Engineering ◽

10.1155/2019/7051248 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Rui Chi ◽

Yixin Su ◽

Zhijian Qu ◽

Xuexin Chi

Keyword(s):

Differential Evolution ◽

Optimization Algorithm ◽

Location Problem ◽

Cuckoo Search ◽

Distribution Center ◽

Metaheuristic Algorithms ◽

Logistics Distribution ◽

Center Location ◽

Mutation Operation ◽

Distribution Center Location

The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search-differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.

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A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Computational Intelligence and Neuroscience ◽

10.1155/2021/6625438 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Julius Beneoluchi Odili ◽

A. Noraziah ◽

M. Zarina

Keyword(s):

Performance Analysis ◽

Optimization Algorithm ◽

Travelling Salesman Problem ◽

African Buffalo ◽

Ant System ◽

Travelling Salesman ◽

Comparative Performance ◽

Extremal Optimization ◽

Insertion Algorithm ◽

Asymmetric Travelling Salesman Problem

This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

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