Solving non-convex economic dispatch problem via backtracking search algorithm

Modiri-Delshad, Mostafa; Rahim, Nasrudin Abd

doi:10.1016/j.energy.2014.09.009

Cited by 102 publications

(40 citation statements)

References 40 publications

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“…Guney, Durmus, and Basbug (2014) and El-Fergany (2015) applied BSA to solve concentric circular antenna array synthesis optimization problems and multi-type distributed generators allocation optimization problems. For non-convex economic dispatch problems, Modiri-Delshad and Rahim (2014) verified BSA's excellent robustness and high efficiency in dealing with this kind of large-scale problem. Askarzadeh and dos Santos Coelho (2014) combined BSA with Burger's chaotic map (BCM) to solve parameter estimation problems of PEMFC electrochemical model.…”

Section: Introductionmentioning

confidence: 99%

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

Zhang

Lin

Gao

et al. 2015

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

Zhang

Lin

Gao

et al. 2015

Expert Systems with Applications

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“…These methods obtained solutions that are still far from reliable, fast, and optimum solutions. They are not always effective and cannot guarantee the global optimal solution for different nonconvex and nonsmooth objective functions in power systems …”

Section: Introduction With Brief Overview Of Proposed Algorithms For mentioning

confidence: 99%

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Ghasemi

Ghavidel

Aghaei

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110‐unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well‐organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems.

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“…Table 7 shows the comparison of the statistical results of different algorithms. In the table, the results obtained by BLPSO are compared with CLPSO, SLPSO, EHNN [65], λ-iteration [64], HM [64], GSO [66], CQGSO [66], BBO [22], BSA [22], and CBA [58]. It can be seen that the best fuel cost obtained by BLPSO is the least of all methods, and the mean fuel cost is the least of all methods with the sole exception of CBA [58].…”

Section: Test Systemmentioning

confidence: 99%

“…Transmission losses are included in this system. The cost coefficient and B coefficient data are taken from [22,64] and listed in Table S3. Table 6 presents the optimal generation values and fuel cost obtained by the BLPSO.…”

Section: Test Systemmentioning

confidence: 99%

“…The first category consists of methods applied in their original version, such as genetic algorithm (GA) [7], particle swarm optimization (PSO) [8], differential evolution (DE) [9], ant colony optimization (ACO) [10], harmony search (HS) [11], artificial bee colony (ABC) [12], teachinglearning-based optimization (TLBO) [13], gravitational search algorithm (GSA) [14], firefly algorithm (FA) [15], biogeography-based optimization (BBO) [16,17], bacterial foraging optimization (BFO) [18], imperialist competitive algorithm (ICA) [19], seeker optimization algorithm (SOA) [20], grey wolf optimization (GWO) [21], backtracking search algorithm (BSA) [22], and root tree optimization (RTO) [23].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

2018

Complexity

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Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeographybased learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeographybased learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

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Solving non-convex economic dispatch problem via backtracking search algorithm

Cited by 102 publications

References 40 publications

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

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