An Evolutionary Parallel Tabu Search approach for distribution systems reinforcement planning |
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| Affiliation: | 1. Information and Technology Studies, Faculty of Education, The University of Hong Kong, Hong Kong, China;2. Community AIDS Response, Norwood, Johannesburg, South Africa;3. School of Health Sciences, Monash University South Africa, Ruimsig, Johannesburg, South Africa;1. Texas Scottish Rite Hospital for Children, 2222 Welborn Street, Dallas, TX 75219, USA;2. Texas Tech Health Science Center, Paul L. Foster School of Medicine, 5001 El Paso Dr, El Paso, TX 79905, USA;3. Children''s Hospital Colorado, 13123 E 16th Ave, Aurora, CO 80045, USA;1. Civil Engineering Department of the Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal;2. Civil Engineering Department of the University of Aveiro, Portugal;3. Economics, Management and Industrial Engineering Department of the University of Aveiro, Portugal;1. Department of Systems Engineering, Northeastern University, Shenyang 110819, PR China;2. College of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 110625, PR China |
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| Abstract: | In this paper a new meta-heuristic optimisation technique is proposed. The method is based on the Parallel Tabu Search (PTS) algorithm and the application is the optimal electrical distribution systems reinforcement planning through the installation of photovoltaic plants, parallel cables, capacitor banks and transformers. The issue is a combinatorial optimisation problem; the objective function is a non-linear expression of a large number of variables. In these cases, meta-heuristics have proved to work well and one of the most efficient is the Tabu Search algorithm. For large-scale problems, parallelisation improves Tabu Search computational efficiency as well as its exploration ability. In this paper, an enhanced version of PTS, Evolutionary Parallel Tabu Search (EPTS), is proposed. It performs reproduction operators on sub-neighbourhoods directing the search towards more promising areas of the search space. The problem of distribution systems reinforcement planning has been studied in detail and the results of the application show that the EPTS outperforms the PTS and Particle Swarm Optimisation algorithms.The algorithm's performance is also tested on mathematical test functions and other properties of the proposed algorithm are examined. |
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