Migration planning using modified Cuckoo Search Algorithm

Saelim, Akajit; Rasmequan, Suwanna; Kulkasem, Pusit; Chinnasarn, Krisana; Rodtook, Annupan

doi:10.1109/iscit.2013.6645935

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Other forms of fitness can be defined in a similar way to the fitness function in genetic algorithms. Generally, the CSA method consists of two important operations including (1) laying egg and (2) destroying and rebuilding nest [28].…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Nguyen

2015

International Journal of Electrical Power & Energy Systems

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Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Nguyen

2015

International Journal of Electrical Power & Energy Systems

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“…For the first method, Uroš Mlakar et al [33] proposed a hybrid self-adaptive CS algorithm (HSA-CS) with which the search space was explored using three exploration search strategies, i.e., random long-distance search strategy, stochastic moderate-distance search strategy and stochastic short-distance search strategy. Saelim et al [34] proposed crossover and random replacement for generating new solutions in place of Lévy flight, and the switching parameter pa was treated as being adaptive. The proposed algorithm was used to solve seven TSPs, and outperformed the original CS.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

et al. 2018

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The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

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Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling

Nguyen

2017

Swarm and Evolutionary Computation

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Migration planning using modified Cuckoo Search Algorithm

Cited by 6 publications

References 8 publications

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Modified cuckoo search algorithm for short-term hydrothermal scheduling

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling

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