Multiobjective bacteria foraging algorithm for electrical load dispatch problem

• Published in: Energy conversion and management Vol. 52; no. 2; pp. 1334 - 1342
• Main Authors: Panigrahi, B.K., Pandi, V. Ravikumar, Sharma, Renu, Das, Swagatam, Das, Sanjoy
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2011; Elsevier
• Subjects: Algorithms; Applied sciences; Bacteria; Bacterial foraging; chemotaxis; Constraints; Demand; Economic data; Economics; Electric energy; Electric generators; Energy; Energy economics; Environmental/economic dispatch; Exact sciences and technology; Forages; foraging; fossil fuels; General, economic and professional studies; Global warming; Methodology. Modelling; Multiobjective optimization; Non-dominated sorting; Optimization; Pareto front; system optimization; Environmental/economic dispatch; Pareto front; Bacterial foraging; Multiobjective optimization; Non-dominated sorting
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2010.09.031

In this paper the bacteria foraging meta-heuristic is extended into the domain of multiobjective optimization. In this multiobjective bacteria foraging (MOBF) optimization technique, during chemotaxis a set of intermediate bacteria positions are generated. Next, we use pareto non-dominance criterion to determine final set of bacteria positions, which constitute the superior solutions among current and intermediate solutions. To test the efficacy of our proposed algorithm we have chosen a highly constrained optimization problem namely economic/emission dispatch. Economic dispatch is a constrained optimization problem in power system to distribute the load demand among the committed generators economically. Now-a-days environmental concern that arises due to the operation of fossil fuel fired electric generators and global warming, transforms the classical economic load dispatch problem into multiobjective environmental/economic dispatch (EED). In the proposed work, we have considered the standard IEEE 30-bus six-generator test system on which several other multiobjective evolutionary algorithms are tested. We have also made a comparative study of the proposed algorithm with that of reported in the literature. Results show that the proposed algorithm is a capable candidate in solving the multiobjective economic emission load dispatch problem.