MSFLA/GHS/SFLA-GHS/SDE algorithms for economic dispatch problem considering multiple fuels and valve point loadings

Vaisakh, K.; Reddy, A. Srinivasa

doi:10.1016/j.asoc.2013.07.001

Cited by 31 publications

(30 citation statements)

References 26 publications

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“…This table reveals that the best cost of MSFS has equally optimal solution quality with eight compared methods and lower cost than two other ones, such as ELANN [10] and IQPSO [28]. However, MSFS is ranked at the first position amongst these methods because of having the lowest value of N Ger with 1.200 while AIS [12] and MPSO [27] have used 3000, DE [5] has used 12.000, IQPSO [28] has used 40.000, and MSFLA, GHS, SFLA-GHS, and SDE [38] have used 9.000. Clearly, MSFS is the standout method with the lowest evaluation for these four cases.…”

Section: Test System 3 With 10 Unitsmentioning

confidence: 99%

“…Therefore, many researchers have chosen outstanding algorithms and combined them to create new algorithms with more promising results than the original algorithms. For this purpose, many articles have productively introduced and launched highly effective algorithms for the ELD problem, such as hybrid PSO with real-valued mutation (RVM-PSO) [35], combination of the differential evolution and particle swarm optimization algorithms (DEPSO) [36], hybrid PSO with gravitational search algorithm (HPSO-GSA) [37], modified shuffled frog leaping algorithm (MSFLA) [38], global best harmony search algorithm (GHS) [38], hybrid SFLA-GHS algorithm [38], combination of hybrid SFLA-GH and shuffled differential evolution (MSFLA/GHS/SFLA-GHS/SDE) [38], hybrid fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm and sequential quadratic programming (SQP) technique (FCASO-SQP) [39], a new hybridization of particle swarm optimization with dynamic linkage discovery (PSO-RDL) [40], hybrid ant colony optimization and real-coded genetic algorithm (GAAIP) [41] and shuffled differential evolution (SED) [42]. All of the previous methods showed that they were powerful and effective techniques that could deal with most of such difficulties of classical methods.…”

Section: Introductionmentioning

confidence: 99%

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A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

et al. 2019

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This paper proposes applications of a modified stochastic fractal search algorithm (MSFS) to solve the economic load dispatch problem (ELD) in which valve-point effects, prohibited operating zones, power losses in all conductors, multi-fuel sources and other constraints of power system are taken into consideration. The proposed method is first developed in the study by performing two modifications on two procedures of new solution generation from conventional stochastic fractal search (SFS). The first modification is used to change the strategy of producing new solutions of the first and the second update procedures while the second one is to newly update the worst solutions in the first update process and the best solutions in the second update process. These modifications have major influence on the solution search performance of the proposed method. All improvements of the proposed method can be illustrated by solving and analyzing results from various test systems with different system scales including 3-unit, 6-unit, 10-unit, and 20-unit systems. Comparison of results obtained by MSFS, SFS, and other existing methods indicates that the proposed MSFS method is more effective and robust than compared methods in terms of solution quality, high-quality solution search stability and convergence process. Consequently, the proposed method should be used as a very favorable optimization method for the ELD problem and it should be tried for other optimization problems in electrical engineering.

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Section: Test System 3 With 10 Unitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

et al. 2019

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“…Tables 1-4 indicate the power generation schedule and generation cost obtained by the proposed SDE for the load demands of 2700 MW, 2600 MW, 2500 MW, and 2400 MW respectively. In Table 1, the effectiveness of SDE in solving convex PED problem, for the power demand of 2700 MW, has been validated by comparing its results with other optimizers in the literature such as Modified Shuffled Frog Leaping Algorithm (MSFLA) [40], Modified Hopfield Neural Network (MHNN) [41], Self-adaptive Differential Evolution (SaDE) [32], and Improved Evolutionary Programming (IEP) [42]. Similarly, Table 2 shows the simulation result of SDE for the power demand of 2600 MW, compared with Hopfield Lagrange Network (HLN) [43], Lamda-Iteration (LI) [43], and SaDE [32].…”

Section: System 1: 10 Machine Multiple Fuel Convex Ped (Without Valvementioning

confidence: 99%

“…of iterations = 800, Crossover rate (CR) = 0.6, Mutation factor (MF) = 0.5 and the results are presented after 30 trials. Table 6 shows the results obtained from the proposed SDE in solving non-convex PED problem for the 2700 MW power demand and are compared to other optimizers in literature such as Improved Genetic Algorithm with Multiplier Updating (IGA_MU) [28], Modified Shuffled Frog Leaping Algorithm (MSFLA) [40], Particle Swarm 1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177 185 193 2400MW 2500MW 2600 MW Figure 5. Convergence-characteristics of SBE algorithm for system 1 (without valve point loading effects), P d = 2400 MW, 2500 MW, 2600 MW.…”

Section: System 2: 10 Machine Multiple Fuel Non-convex Ped (With Valvmentioning

confidence: 99%

“…Table 6 shows the results obtained from the proposed SDE in solving non-convex PED problem for the 2700 MW power demand and are compared to other optimizers in literature such as Improved Genetic Algorithm with Multiplier Updating (IGA_MU) [28], Modified Shuffled Frog Leaping Algorithm (MSFLA) [40], Particle Swarm Optimization (PSO) [49], conventional DE [49], Real-coded Genetic Algorithm (RGA) [49], New Particle Swarm Optimization with Local Random Search (NPSO-LRS) [50], Back-tracking Search Algorithm (BSA) [51], Cuckoo Search Algorithm with Cauchy distribution (CSA-Cauchy) [52] and BAT [16]. Table 7 shows the simulation results of SDE for the power demand of 2600 MW and are compared with conventional PSO [49], RGA [49], DE [49], MSFLA [40], Global-best Harmony Search (GHS) [40], BAT [16], SaDE [32]. In Table 8, SDE simulation results have been compared with DE [49], RGA [49], PSO [49] and Adaptive Simulated Annealing (ASA) [53] for 2500 MW load demand.…”

Section: System 2: 10 Machine Multiple Fuel Non-convex Ped (With Valvmentioning

confidence: 99%

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Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

et al. 2018

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This paper presents an optimization method for solving the Power Economic Dispatch (PED) problem of thermal generation units with multiple fuels and valve point loadings. The proposed optimizer is a variant of Differential Evolution (DE) characterized as a Stud Differential Evolution (SDE), which has been proposed earlier and implemented on a hydrothermal energy system. In SDE, an operator named Stud Crossover (SC) is introduced in the conventional DE during the trial vector updating process. In SC operator, a best vector gives its optimal information to all other population members through mating. The proposed algorithm's effectiveness to solve Multiple Fuel PED problem, with and without Valve Point Loading Effects (VPLEs), has been validated by testing it on 10 machine multiple fuel standard test systems having 2400 MW, 2500 MW, 2600 MW, and 2700 MW load demands. The results depict the strength of SDE over various other methods in the literature.

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Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

Secui,

Secui

2024

Appl Intell

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Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals’ evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time.

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MSFLA/GHS/SFLA-GHS/SDE algorithms for economic dispatch problem considering multiple fuels and valve point loadings

Cited by 31 publications

References 26 publications

A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

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