Multi-objective thermal power load dispatch using chaotic differential evolutionary algorithm and Powell’s method
In this work, a chaotic differential evolutionary and Powell’s pattern search (CDEPS) algorithm is proposed to solve multi-objective thermal power load dispatch (MTPLD) problem. The chaotic differential evolutionary method is responsible for the diversification, and Powell’s pattern search is dedicated to exploitation. Further, the performance of two CDEPS variants based on Gauss map and Tent map is investigated. The proposed MTPLD solution procedure either identifies a solution close to Pareto front or diversifies the existing Pareto frontier and finally selects a suitable compromising solution among the available options. In order to select the best compromising solution, a combination of surrogate worth trade-off approach and fuzzy theory is proposed as choice of objectives is ambiguous. The uniformity of Pareto front is evaluated by exploiting a quality measure approach. The performance analysis is done using generalized benchmark test functions and complex MTPLD problems. The ability of CDEPS to diversify Pareto front is verified by uniformity analysis of Pareto front. The one-sample Wilcoxon’s test and two-sample Mann Whitney’s test are used to analyze the experimental results. The exhaustive analysis shows that the Tent map-based CDEPS has better ability to generate quality generation schedule with uniform Pareto front quality and faster convergence rate.
KeywordsChaotic differential evolutionary algorithm Powell’s pattern search Surrogate worth trade-off method Multi-objective optimization Thermal power load dispatch
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
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