A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect

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Duman S., Yörükeren N., Altaş İ. H.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, cilt.64, ss.121-135, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 64
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1016/j.ijepes.2014.07.031
  • Dergi Adı: INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.121-135
  • Anahtar Kelimeler: Benchmark test functions, Non-convex economic dispatch, Hybrid PSOGSA, Fuzzy logic, Optimization, PARTICLE SWARM OPTIMIZATION, SHUFFLED DIFFERENTIAL EVOLUTION, LOAD DISPATCH, GENETIC ALGORITHM, WAVELET MUTATION, SEARCH ALGORITHM, STRATEGY, COLONY, SQP
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Gravitational Search Algorithm (GSA) is a novel stochastic optimization method inspired by the law of gravity and interaction between masses. This paper proposes a novel modified hybrid Particle Swarm Optimization (PSO) and GSA based on fuzzy logic (FL) to control ability to search for the global optimum and increase the performance of the hybrid PSOGSA. In order to test the performance of the modified hybrid PSOGSA based on FL (FPSOGSA), it has been applied to solve the well-known 23 benchmark test functions. In order to evaluate the efficiency and performance of the proposed approach, standard power systems including IEEE 5-machines 14-bus, IEEE 6-machines 30-bus, 13 and 40 unit test systems are used. These are non-convex economic dispatch problems including the valve-point effect and are computed with and without the losses. The results obtained from the proposed FPSOGSA approach are compared with those of the other heuristic techniques in the literature. The results of the comparison demonstrate that the proposed approach can converge to the near optimal solution and improve the performance of the standard hybrid PSOGSA approach. (C) 2014 Elsevier Ltd. All rights reserved.