A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

Abstract

The economic dispatch (ED) problem aims to minimize the total generation cost while satisfying certain constraints, such as valve-point effects, multi-fuel options, prohibited operating zones, transmission losses, and ramp rate limits. In this paper, these constraints are considered simultaneously for the first time, resulting in a complex nonconvex ED problem. A diversity-based parallel particle swarm optimization (DPPSO) is proposed to solve the nonconvex ED problem, where the implementation details—such as evaluation function design, particle definition, and equality and inequality handling strategies—have been carefully discussed. In our approach, the population of DPPSO is divided into different groups to maintain diversity in particles so that the optimization capacity can be enhanced. An asynchronous information–sharing mechanism (AISM) helps decrease the population size. Hence, the computational burden is reduced. Moreover, information in different groups is calculated parallelly and updated asynchronously to improve computational efficiency. Benchmark functions are employed to demonstrate the effectiveness of the proposed method. Furthermore, three nonconvex ED problems are resolved by the proposed method, and state-of-the-art performance has been achieved. In addition, the proposed algorithm is highly modular, making it easy to unite other salient variants of particle swarm optimization (PSO) to improve its performance.

A hybrid quantum-behaved particle swarm optimization solution to non-convex economic load dispatch with multiple fuel types and valve-point effects

“…In these systems, the ramp rate limit, the prohibited zones, the valve-points and multi fuel options of the equipment were taken into consideration in the corresponding experiment. For a comprehensive performance comparison, many optimization algorithms were evaluated on these four systems, including, the standard PSO with shrinkage and inertia weight (SPSO) [38], the hybrid gradient descent PSO (HGPSO) [38], the QPSO [39], the hybrid PSO with mutation (HPSOM) [38], the hybrid PSO with wavelet mutation (HPSOWM) [38], the chaos PSO (CPSO) [40], artifcial bee colony algorithm with distance-ftness-based neighbor search (DFnABC) [41], enhanced self-adaptive global-best harmony search (ESGHS) [42], diversity-based parallel PSO (DPPSO) [47]and differential evolutioncrossover QPSO (DE-CQPSO) [33]. For each system, all the tested methods used the same objective function.…”

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

“…Metaheuristic algorithms (MAs) can be classified into four distinct categories, which include evolutionary algorithms (EAs), swarm intelligence (SI) methods, approaches based on natural phenomena, and algorithms inspired by human behavior [170]. Over a few decades, numerous MAs have been developed, among which genetic algorithm (GA) [129,[171][172][173][174][175], particle swarm optimization (PSO) [176][177][178][179][180][181], ant colony optimization (ACO) [182][183][184][185], and artificial bee colony (ABC) [186][187][188][189][190][191] and their variants have been extensively applied to solve the EDP. The main feature of these nature-inspired algorithms is their reliance on searching the space of potential solutions to find optimal or near-optimal outcomes.…”