An immune algorithm with power redistribution for solving economic dispatch problems

Aragón, Victoria S.; Esquivel, Susana Cecilia; Coello, Carlos A. Coello

doi:10.1016/j.ins.2014.10.026

Cited by 87 publications

(38 citation statements)

References 49 publications

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“…The main difficulties of this system for any optimization algorithm are the nonlinear and noncontinuous decision space and the power balance constraint with network transmission losses. From Table 5, it clearly shows that the proposed method obtains the best result with lowest FEs than other techniques except for IA_EDP [7] in the 15-generating-unit test systems. However, it should be noted that the exact power loss computed from the best solution found by IA_EDP [7] is actually 30.2825 instead of 30.0187 as reported in the corresponding literature, which shows that the total generated power of the schedule is much less than the total load demand plus its total line loss; obviously, the best solution reported in [7] is in fact infeasible.…”

Section: Case Studies and Resultsmentioning

confidence: 92%

“…In recent decades, a wide variety of metaheuristic optimization methods such as genetic algorithm (GA) [4,5], artificial immune system (AIS) [6,7], particle swarm optimization (PSO) [8][9][10][11][12][13][14][15][16], differential evolution (DE) [17][18][19], gravitational search algorithm (GSA) [20], Tabu Search (TS) [21,22], neural network (NN) [23,24], evolutionary programming (EP) [25], bacterial foraging algorithm (BFA) [26], biogeography-based optimization (BBO) [27], and other population-based optimization algorithms [28][29][30][31][32] have been applied with success in solving the ED problems and been able to obtain better solutions compared to using conventional optimization methods.…”

Section: Introductionmentioning

confidence: 99%

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An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Yang

2018

Complexity

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Population-based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve economic dispatch (ED) problems. It is a complex and highly nonlinear constrained optimization problem. The proposed approach, double elitist breeding quantum-based particle swarm optimization (DEB-QPSO), makes use of two elitist breeding strategies to promote the diversity of the swarm so as to enhance the global search ability and an improved efficient heuristic handling technique to manage the equality and inequality constraints of ED problems. Investigating on 15-unit, 40-unit, and 140-unit widely used test systems, through performance comparison, the proposed DEB-QPSO algorithm is able to obtain higher-quality solutions efficiently and stably superior than the other the state-of-the-art algorithms.

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Section: Case Studies and Resultsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Yang

2018

Complexity

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“…From numerical results, it is shown that the proposed AIS provides a more efficient solution than do particle swarm optimization and evolutionary programming in terms of minimum cost and computation time. Aragón et al [32] present an AIS-inspired algorithm, called IA EDP, which tries to solve an economic dispatch problem. It makes use of two versions of a redistribution power operator which tries to keep the solutions that it finds.…”

Section: Ais Applications In Energymentioning

confidence: 99%

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

et al. 2018

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Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints.

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“…Then realcoded GA was proposed for continuous multi-dimensional problem [7,8]. Modern heuristic algorithms such as Harmony search algorithm (HSA) [9], modified Differential evolution algorithm [10], Cuckoo search algorithm [11], Immune algorithm (IA) [12], Evolutionary programming [13] are tested and reported on various single and multi-area economic dispatch problems. Combined multi-area problem [14] with emission constraint was proposed using chaotic artificial bee colony (CABC) methodology.…”

Section: Introductionmentioning

confidence: 99%

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

Lakshminarasimman¹,

Siva²,

Balamurugan³

2017

IJCA

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This paper presents the Water Wave Optimization Algorithm (WWOA) for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, area demand constraints, multiple fuels options, valvepoint loading effects and prohibited operating zones. Here, the amount of power that can be economically generated in one or more areas are exchanged with other areas with deficient generation through the interconnected tie-lines while meeting out the area wise and total power demand and other constraints is formulated as the MAED problem. WWOA is one of the nature inspired algorithm which mimics the phenomena of water waves for global optimization is implemented for the solution of multi-area economic dispatch problem. The effectiveness of the proposed algorithm has been verified on three different test systems and are compared with Teaching learning based optimization (TLBO), differential evolution (DE), evolutionary programming (EP) and real coded genetic algorithm (RCGA), considering the quality of the solution obtained, and the results shows a quick convergence of the proposed algorithm and are found to be superior than the other methods in the literature and seems to be a potential alternative advancement in practical power system for solving the MAED problems. KeywordsWater wave optimization algorithm, multi-area economic dispatch, multiple fuel options, cost minimization, prohibited operating zones.

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An immune algorithm with power redistribution for solving economic dispatch problems

Cited by 87 publications

References 49 publications

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

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