Economic Load Dispatch Using a Chemotactic Differential Evolution Algorithm

Abstract

Abstract. This paper presents a novel stochastic optimization approach to solve constrained economic load dispatch (ELD) problem using Hybrid Bacterial Foraging-Differential Evolution optimization algorithm. In this hybrid approach computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossover of the optimization agents. The proposed methodology easily takes care of solving non-convex economic load dispatch problems along with different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.

A novel differential evolution algorithm for economic power dispatch problem

“…where pwr D is total load demand (in MW); pwr L is transmission loss (in MW). The transmission losses are represented as a quadratic function in terms of constant loss formula coefficient or B-coefficient and generators power [6] which are associated in the form,…”

“…A selforganizing hierarchical particle swarm optimization (SPSO) has been designed by Chaturvedi et al [8]. Biswas et al proposed a chemotactic differential evolution algorithm [6], which strengthens its global search ability. Safari et al used iteration particle swarm optimization (IP-SO) procedure for economic load dispatch with generator constraints [29] to prevent local optima problem.…”

A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

“…Among the evolutionary algorithms, the genetic algorithm (GA) [38], evolutionary strategy (ES) [39], evolutionary programming (EP) [5,29], and genetic programming (GP) [40] are classical paradigms in evolutionary computing field [3]. Differential evolution (DE) [41], particle swarm optimization (PSO) [24], and ant colony optimization (ACO) [23,42,43], cuckoo search (CS) [44], and teaching learningbased optimization (TLBO) algorithm [45,46] are comparatively recently developed EAs and successfully applied to various test suites of optimization problems and many realworld problems [18,[47][48][49][50].…”

Economic Load Dispatch by Hybrid Swarm Intelligence Based Gravitational Search Algorithm

“…Due to the inadequacy of these methods to stuck to the local solution instead of global ones, artificial intelligence techniques are used to solve ELD problem, these techniques include Genetic algorith m (GA) [8], Particle Swarm (PSO) [8], Evolutionary Programming (EP) [9], Differential Evolution (DE) [10], Hopfield neural network (HNN) [11]. Other techniques are New Particle Swarm with Local Rando m Search (NPSO_LRS) [12], Self-Organizing Hierarchical Particle Swarm Optimizat ion (SOH_PSO) [13], Bacterial Foraging Optimization Nelder Mead Hybrid Algorith m (BFONM) [14], Biogeography based optimization (BBO) [15], continuous Quick Group Search Optimizer (QGSO) [16], Chemo tactic Differential Evo lution Algorith m (BF_DE hybrid) [17], Hybrid swarm intelligence harmony search (HHS) [18], Firefly algorith m (FA) [19], Artificial bee colony optimization(ABC) [22].These optimization methodologies have been applied successfully to solve economic load dispatch problem.…”