2015
DOI: 10.3906/elk-1305-258
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Opposition-based gravitational search algorithm applied to economic power dispatch problems consisting of thermal units with emission constraints

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Cited by 14 publications
(5 citation statements)
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References 23 publications
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“…As is seen from Fig. 4, the best comprise solutions of NSGA- [17], FPA [5] are located above the Pareto-front clearly and the ones of MODE [22], PDE [22], SPEA2 [22], GSA [29] and v-MOGA [30] are also above the Pareto-front and locate near the solution of KSO when w = 0.5, which implies that the solution of KSO dominates the best comprise solutions of these algorithms. Meanwhile, the best comprise solutions of BSA [31], QOTLBO [16], TLBO [16], OGHS [32], NGPSO [27] are just on the Pareto-front which mean the competitive performance of KSO with them.…”
Section: Resultsmentioning
confidence: 84%
“…As is seen from Fig. 4, the best comprise solutions of NSGA- [17], FPA [5] are located above the Pareto-front clearly and the ones of MODE [22], PDE [22], SPEA2 [22], GSA [29] and v-MOGA [30] are also above the Pareto-front and locate near the solution of KSO when w = 0.5, which implies that the solution of KSO dominates the best comprise solutions of these algorithms. Meanwhile, the best comprise solutions of BSA [31], QOTLBO [16], TLBO [16], OGHS [32], NGPSO [27] are just on the Pareto-front which mean the competitive performance of KSO with them.…”
Section: Resultsmentioning
confidence: 84%
“…Best fuel cost (w = 1.0) Best pollution emission (w = 0.0) which also illustrates the best comprising solutions obtained by some algorithms in the literature. As seen from Figure 5, the best comprising solutions of NSGA [21] and FPA [13] were located above the Pareto front clearly and those of MODE [23], PDE [23], SPEA2 [23], GSA [8], and ϵv-MOGA [29] were also above the Pareto front and located near the solution of FSO when w = 0.5, which implied that the solution of FSO dominated the best comprising solutions of these algorithms. Meanwhile, the best comprising solutions of BSA [30], QOTLBO [20], TLBO [20], OGHS [31], and NGPSO [27] were just on the Pareto front, which showed the competitive performance of FSO with them.…”
Section: Algorithmmentioning
confidence: 92%
“…In general, there are mainly two approaches. One is converting the multiobjective problem into a single objective optimization problem and solving it with metaheuristic algorithms, such as the genetic algorithm (GA) [5], particle swarm optimization algorithm (PSO) [6], differential evolution algorithm (DE) [7], opposition-based gravitational search algorithm (OGSA) [8], opposition-based harmony search algorithm (OHS) [9], spiral optimization algorithm (SOA) [10], virus optimization algorithm (VOA) [11], moth swarm algorithm (MSA) [12], flower pollination algorithm (FPA) [13], modified bacterial foraging algorithm (MBFA) [14], and charged system search algorithm (CSS) [15].…”
Section: Introductionmentioning
confidence: 99%
“…The process that GA undergoes until it comes to a solution can be described as coding the solution set, creating the initial population, assessing the compatibility of the solutions in the population, choosing the progenitor individuals according to the compatibility and creating new individuals through crossover and mutation processes. As for the control parameters of the GA, the crossover rate and the mutation rate are selected between 0.5-1.0 and 0.0001-0.05, respectively [20][21][22][23].…”
Section: Genetic Algorithmmentioning
confidence: 99%