A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch

Multi-objective optimization is an interesting and hot topic in the literature involving the conflicting objectives to be solved simultaneously. In this study, a new multiple optimization method based on a discrete bacterial algorithm is developed to address the multi-objective economic-environmental dispatch problem with non-linear, non-convex, and complexity constraints. In the proposed multi-objective bacterial based algorithm, the existence of bacteria complies with a fitness survival mechanism, in which a health sorting approach is operated to control the chances of reproduction as well as elimination. The performances of bacteria have been recorded and sorted for health evaluation, which can help to group the individuals according to their search capability and improve the overall quality of the population. To speed up the convergence rate and avoid local minima to some extent, a comprehensive learning strategy is embedded to enable the communication exchanges between the bacteria and external archive. The standard IEEE 30-bus, 6-generator test system is adopted to illustrate the efficiency of the proposed method by making the comparison with the other multiple bacterial-based algorithms as well as six other well developed evolutionary algorithms. The effectiveness of the propose method is well validated in experiments by providing similar or superior solutions to environmental/economic power dispatch issues considering the various constraints.

[1]  Jay Prakash,et al.  NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering , 2016, Neurocomputing.

[2]  Lixiang Li,et al.  A multi-objective chaotic particle swarm optimization for environmental/economic dispatch , 2009 .

[3]  Ying Wang,et al.  Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects , 2011, Eng. Appl. Artif. Intell..

[4]  Bijay Ketan Panigrahi,et al.  A Multiobjective Bacterial Foraging Algorithm to Solve the Environmental Economic Dispatch Problem , 2014 .

[5]  Hossein Nezamabadi-pour,et al.  A modified particle swarm optimization for economic dispatch with non-smooth cost functions , 2010, Eng. Appl. Artif. Intell..

[6]  Marko Cepin,et al.  A multi-objective optimization based solution for the combined economic-environmental power dispatch problem , 2013, Eng. Appl. Artif. Intell..

[7]  Li Li,et al.  Multi-objective particle swarm optimization based on global margin ranking , 2017, Inf. Sci..

[8]  Prakash Kumar Hota,et al.  Economic emission load dispatch through fuzzy based bacterial foraging algorithm , 2010 .

[9]  Matthew Collette,et al.  A multi-objective variable-fidelity optimization method for genetic algorithms , 2013 .

[10]  Zhigang Lu,et al.  A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis , 2014 .

[11]  Lingfeng Wang,et al.  Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search , 2009, Eng. Appl. Artif. Intell..

[12]  L. H. Wua,et al.  Environmental/economic power dispatch problem using multi-objective differential evolution algorithm , 2010 .

[13]  Bijay Ketan Panigrahi,et al.  Multiobjective bacteria foraging algorithm for electrical load dispatch problem , 2011 .

[14]  Gwo-Ching Liao,et al.  A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reducti , 2011 .

[15]  Ben Niu,et al.  Multi-objective bacterial foraging optimization , 2013, Neurocomputing.

[16]  Mohsen Niasati,et al.  Multi objective optimal allocation of fault current limiters in power system , 2017 .

[17]  Yun Tian,et al.  An improved bacterial colony chemotaxis multi-objective optimisation algorithm , 2013, Int. J. Comput. Sci. Math..

[18]  S. Baskar,et al.  An improved generalized differential evolution algorithm for multi-objective reactive power dispatch , 2012 .

[19]  Patrick Siarry,et al.  Robust RST control design based on Multi-Objective Particle Swarm Optimization approach , 2016 .

[20]  Manoj Kumar Tiwari,et al.  Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch , 2008, IEEE Transactions on Evolutionary Computation.

[21]  V. Ravikumar Pandi,et al.  A hybrid multi-objective improved bacteria foraging algorithm for economic load dispatch considering emission , 2015, Int. J. Comput. Sci. Eng..

[22]  Zhile Yang,et al.  A novel hybrid teaching learning based multi-objective particle swarm optimization , 2017, Neurocomputing.

[23]  Analía Amandi,et al.  Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan , 2013 .

[24]  Maoguo Gong,et al.  Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering , 2017, Pattern Recognit..

[25]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[26]  Dun-Wei Gong,et al.  A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch , 2012, Inf. Sci..

[27]  Mohammad Sadegh Helfroush,et al.  A fuzzy multi-objective hybrid TLBO-PSO approach to select the associated genes with breast cancer , 2017, Signal Process..

[28]  Yaonan Wang,et al.  Operating Point Optimization of Auxiliary Power Unit Using Adaptive Multi-Objective Differential Evolution Algorithm , 2017, IEEE Transactions on Industrial Electronics.

[29]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[30]  A. A. Abido,et al.  A new multiobjective evolutionary algorithm for environmental/economic power dispatch , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[31]  Alberto Delgado,et al.  A novel multiobjective optimization algorithm based on bacterial chemotaxis , 2010, Eng. Appl. Artif. Intell..

[32]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[33]  Ferial El-Hawary,et al.  A summary of environmental/economic dispatch algorithms , 1994 .

[34]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[35]  Josefa Mula,et al.  Fuzzy multi-objective optimisation for master planning in a ceramic supply chain , 2012 .

[36]  Lingfeng Wang,et al.  Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization , 2008 .

[37]  Bijay Ketan Panigrahi,et al.  Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatc , 2010 .

[38]  Lingfeng Wang,et al.  Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm , 2007 .

[39]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.