An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects. (28th October 2020) - Parouha, Raghav Prasad; Verma, Pooja
Summary: This paper presents an innovative hybrid algorithm (i h PSODE) for economic load dispatch (ELD) problems with or without valve‐point effects. It integrated with suggested novel PSO (nPSO) and DE (nDE). Where in nPSO, a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE, a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of i h PSODE population, best half member has been identified and rest members discarded. Further, in current population nPSO is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed i h PSODE and its suggested component nPSO and nDE have been tested over IEEE CEC2006 (it comprising linear, nonlinear, polynomial, quadratic, and cubic functions), IEEE CEC2014, and IEEE CEC2017 (these test suites consist of unimodal, multimodal, hybrid, and composition functions) well‐known benchmark functions. Then, the efficacy of the proposed algorithms has been demonstrated on nonconvex economic load dispatch problems which consist of 3‐, 6‐, 15‐, 40‐, and 140‐unit test systems. The comparative results with some of the state‐of‐the‐art algorithms confirm the effectiveness of the proposed algorithms in both aspects of better quality solutions and higher efficiency. Abstract : A widespread survey of numerous meta‐heuristic algorithms has been investigated. A novel PSO (nPSO) and DE (nDE) with their innovative hybrid (i h PSODE) are proposed. Proposed algorithms are successfully tested on IEEE CEC2006, IEEE CEC2014, and IEEE CEC2017 well‐known benchmark functions. Proposed algorithms used to solve nonconvex ELD problems which consist of 3‐, 6‐, 15‐, 40‐, and 140‐unit test systems. Experimental results show that proposed algorithms can acquire better performance.