A hybrid chameleon swarm algorithm with superiority of feasible solutions for optimal combined heat and power economic dispatch problem

Combined heat and power economic dispatch (CHPED) is a challenging important optimization task in the economic operation of power systems that aims to minimize the production cost by scheduling the generation and heat outputs to committed units. The interdependency of heat and power production of the CHPED task exhibits non-convexity and non-linear natures in its modeling and optimization. Therefore, this paper introduces a novel hybrid approach comprising chameleon swarm algorithm (CSA) and mayfly optimization (MO), named CSMO, for solving the CHPED problem. The proposed CSMO algorithm has a better capability to evade from the trapping in local optima with faster rate of convergence pattern than the traditional CSA. Also the proposed CSMO algorithm employs the MO' phase to assist the CSA to search based on deeper exploration/exploitation capabilities as MO utilizes two populations of male and female mayflies with crossover-based matting process. The effectiveness of the proposed CSMO algorithm is validated on CEC 2017 benchmark functions and two systems of the CHPED problem. The obtained results are compared with some successful optimizers. The simulation outcomes are portrayed based on the number of occasions where CSMO performs superior/equal/inferior to the other optimizers by considering the smaller mean values obtained by each algorithm for all test suites. Accordingly, it is exposed that the occasions achieved by the proposed CSMO are 29/1/0, 30/0/0, 30/0/0, 28/2/0, and 30/0/ 0 against some implemented algorithms, i.e., ISA, GOA, GBO, EO, and the original CSA. Similarly, the number of occasions achieved by the proposed CSMO are 30/0/0, 30/0/0, 30/0/0, 30/0/0, 30/0/0, 29/1/0, and 22/2/6 when the simulations are portrayed against some competitors from literature including the PSO, FA, FFPSO, HPSOFF, HFPSO, HGSO, and Q-SCA, respectively. Furthermore, the results of total cost found by CSMO are 9257.07 $/h for system 1 and 10094.25 $/h for system 2 of the CHPED problem, with percentage of improvement 0.02% and 14.42% on the original CSA, respectively. In addition, further assessments based on the Wilcoxon test, and convergence characteristic are reported. Based on the recorded results, it is portrayed that the CSMO can efficiently deal with the CEC 2017 benchmark functions and CHPED problem. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a novel hybrid approach, named CSMO, for solving the combined heat and power economic dispatch (CHPED) problem. The CSMO algorithm combines the chameleon swarm algorithm (CSA) and mayfly optimization (MO). The results show that the CSMO algorithm outperforms other optimizers in terms of convergence rate and cost reduction.