Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch

Abstract

Publication informationEnergy, 44 (1): 228-240Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6166 Publisher's statementThis is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy (VOL 44, ISSUE 1, (2012)

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

“…The convergence curves make clear that the results converged from larger values guarantee that the proposed WEO algorithm is efficient and obtain better results than earlier reported techniques. [10] 1,051,163 NA NA 0.421 EP [10] 1,048,638 NA NA 15.049 HS [12] 1,046,726 NA NA NA DE [11] 1,036,756 1,040,586 1,452,558 0.20 GA [7] 1,033,481 1,038,014 1,042,606 NA SOA [15] 1,023,946 1,026,289 1,029,213 NA AIS [9] 1,021,980 1,023,156 1,024,973 25.346 ABC [13] 1,021,576 1,022,686 1,024,316 2.603 TLA [16] 1,019,925 1,020,411 1,021,118 0.049 ICA [14] 1,018,467 1,019,291 1,021,796 NA HDE [29] 1,031,077 NA NA NA IPSO [17] 1 …”

“…To overcome this deficiency, turn to various heuristic techniques such as Genetic Algorithm (GA) [7], Simulated Annealing (SA) [8], Artificial Immune System (AIS) [9], Evolutionary Programming (EP) [10], Differential Evolution (DE) [11], Harmony Search (HS) [12], Artificial Bee Colony (ABC) [13], Imperialist Competitive Algorithm (ICA) [14], Seeker Optimization Algorithm (SOA) [15], Teaching Learning Algorithm (TLA) [16], Improved Particle Swarm Optimization (IPSO) [17], Chaotic Differential Evolution (IDE) [18], Modified Teaching Learning Algorithm (MTLA) [19], Self-Adaptive Modified Firefly Algorithm (SAMFO) [20], Improve Pattern Search (IPS) [21], Enhanced Cross Entropy (ECE) [25], Adaptive Particle Swarm Optimization (APSO) [28], Enhanced Bee Swarm Optimization (EBSO) [35], Deterministic Guided Particle Swarm Optimization (DGPSO) [37]. The main drawback of these heuristic techniques gives the results but struck the local minima and lack of guarantee of convergence infinite time for large scale DED problems.…”

Direct load control by distributed imperialist competitive algorithm

“…There are many heuristic-based optimization algorithms that can be applied to solve the model, such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolutionary (DE) algorithm, etc. Recently, there is a new algorithm called imperialist competitive algorithm (ICA) [19] proposed has been applied to solve many industrial optimization problems [20][21][22]. A recent paper reported ICA is powerful to solve power system combinatorial problem [23].…”

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

“…Meta-heuristic approaches have consequently been employed to solve these problems. Numerous meta-heuristic algorithms have been used to solve the economic dispatch problem such as particle swarm optimization (PSO) [7], differential evolution (DE) [8], harmony search (HS) [9], biogeography-based optimization (BBO) [10], and imperialist competitive algorithm (ICA) [11], etc.…”