Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

Abstract

Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swarm optimization (CLPSO) can achieve high exploration while it converges relatively slowly on unimodal problems. To enhance the exploitation of CLPSO without significantly impairing its exploration, a multi-leader (ML) strategy is combined with CLPSO. In ML strategy, a group of top ranked particles act as the leaders to guide the motion of the whole swarm. Each particle is randomly assigned with an individual leader and the leader is refreshed dynamically during the optimization process. To activate the stagnated particles, an adaptive mutation (AM) strategy is introduced. Combining the ML and the AM strategies with CLPSO simultaneously, the resultant algorithm is referred to as multi-leader comprehensive learning particle swarm optimization with adaptive mutation (ML-CLPSO-AM). To evaluate the performance of ML-CLPSO-AM, the CEC2017 test suite was employed. The test results indicate that ML-CLPSO-AM performs better than ten popular PSO variants and six other types of representative evolutionary algorithms and meta-heuristics. To validate the effectiveness of ML-CLPSO-AM in real-life applications, ML-CLPSO-AM was applied to economic load dispatch (ELD) problems.

Energy Management of a Power System for Economic Load Dispatch Using the Artificial Intelligent Algorithm

“…This system is a classic case in ELD, which adopts the smooth fuel cost curve [42]. This case study consists of 6 generating units, 46 transmission lines, and 26 buses [43]. The power load demand is 1263 MW, and the detailed parameters of the generator units are given in Table 4.…”

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

“…Another more complicated ELD problem has taken into account active power loss, the valve effects and complex constraints of thermal generating units such as prohibited working zones [3], ramp rate 2 of 35 limitations [4] and generation limitations [5]. The concerned problem has been solved by applying a huge number of methods such as maximum likelihood optima (MLO) [6], evolutionary particle swarm optimization algorithm (EPSO) [7], improved stochastic fractal search algorithm (ISFSA) [8], improved social spider optimization algorithm (ISSOA) [9], interior search algorithm (ISA) [10], multi-leader comprehensive learning particle swarm optimization with adaptive mutation (MLCL-PSO) [11], dragonfly algorithm (DA) [12] and ameliorated grey wolf optimization (AGWO) [13]. In general, these studies focused on demonstrating constraint handling ability and high-quality solution searching ability of original methods and improved methods rather than proposing new issues and real phenomena regarding power systems and electric components.…”

“…Different methods have been proposed for the problem such as Lagrange function-based evolutionary programming (LFEP) [19], Tabu search algorithm (TSA) [20], muller approach (MA) [21], memetic optimization algorithm (MOA) [22], modified artificial bee colony optimization algorithm (MABCOA) [23], ant colony optimization algorithm (ACOA) [24], multi-agent model algorithm (MAMA) [25], binary fish optimization algorithm (BFOA) [26], Lagrange function-based invasive weed optimization algorithm (LFIWOA) [27], sine function and cosine function-based algorithm (SCBA) [28], binary whale optimization algorithm (BWOA) [29], expanded Lagrange function-based Hopfield network method (ELF-HNM) [30] and five Lagrange function-based Hopfield neuron network (LF-HNN) methods [31]. The main difference between studies [1][2][3][4][5][6][7][8][9][10][11][12][13] and studies [20][21][22][23][24][25][26][27][28][29][30][31] is competitive electric market. For example, the same authors have published three studies [8,9,31] in Energies journal but only the study [31] has ...…”

“…PSO is easily fallen into local optimal zones, and the possibility of jumping out the zones and moving to global optimal zones is low. However, it is not denied that PSO has been widely and successfully applied for many optimization problems, and its improved versions have increased constantly such as MLCL-PSO for ELD problem [11], hybrid particle swarm optimization (HPSO) for power loss optimization problem in transmission networks [34], hybrid multiagent-based particle swarm optimization (HMPSO) [35], PG-PSO for ELD problem [36], time-varying acceleration coefficients-based particle swarm optimization (TVAC-PSO) for combined heat and power dispatch (CHPD) problem [37], and four PSO methods such as constriction factor-based particle swarm optimization (CF-PSO), inertia weight factor-based particle swarm optimization (IW-PSO), CF-PSO with local best particle and IW-PSO with local best particle for CHPD problem [38]. As shown in the studies, these PSO methods could reach good result with higher performance than other metaheuristic methods like DE, hybrid DE (HDE), improved DE (IDE), GA, hybrid GA (HGA), TSA, gravitational search algorithm (GSA), harmony search algorithm (HSA), evolutionary programming algorithm (EPA), simulated annealing algorithm (SAA) and biogeography-based optimization algorithm (BBOA).…”

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

“…In recent years, artificial intelligence algorithms have been widely used in dynamic economic dispatching models. For example, genetic algorithm (GA) [3][4][5][6][7][8][9], simulated annealing algorithm (SA) [10], tabu search algorithm (TS) [11,12], differential evolution algorithm (DE) [13][14][15][16][17][18][19], particle swarm optimization (PSO) [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], artificial bee colony algorithm (ABC) [35], artificial immune system algorithm (AIS) [36,37], evolutionary programming algorithm (EP) [38][39][40], complementary quadratic programming algorithm (cQP) [41], biogeography-based optimization algorithm (BBO) [42,43], teaching learning-based optimization algorithm (TLBO) [44,45], charged system search algorithm (CSSA) [46], flower pollination algorithm (FPA) [47], rooted tree optimiz...…”