A hybrid ant colony optimization approach based local search scheme for multiobjective design optimizations

Mousa, Abd Allah A.; El-Wahed, Waiel F. Abd; Rizk‐Allah, Rizk M.

doi:10.1016/j.epsr.2010.12.005

Cited by 39 publications

(23 citation statements)

References 13 publications

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“…The fill factor, k fill , the fraction of slot volume occupied by conductors, is assumed to be equal to 0.6. Formal derivations of the mathematical expressions for the force generated in a LSM and the constraints are carried out in [1]. The analysis is simplified with the following assumptions.…”

Section: Mathematical Problem Statement [1]mentioning

confidence: 99%

“…To increase the force one needs to either increase the flux density, the length of the conductors or the total current. At the same time one has to consider the inherent as well as external limitations that appear in the form of constraints such as: [1,15] a) Heat Constraint: The preliminary calculations for the LSM show that approximately 4000W of heat can be dissipated out of the motor with the proposed arrangement of coils. Considering 4000W as the upper limit on the heat dissipation rate.…”

Section: Shape Design Of a Linear Synchronous Motormentioning

confidence: 99%

“…The flow of current is in the direction perpendicular to both the direction of the motion and the direction of the magnetic flux. [1] This paper intends to present an optimal design of a LSM to replace a hydraulic actuator and design air-cored solenoid using hybrid AIS approach. This methodology combined AIS with Local Search, AIS find initial Pareto front then Local search improve this initial Pareto after that applying mutation process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems

El-Refaey¹

2017

AJNNA

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Abstract:In this paper, an artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and local search (LS) is propose to solve Multiobjective engineering design problems. This paper presents an optimal design of a linear synchronous motor (LSM) considering two objective functions namely, maximum force and minimum saturation and then design of air-cored solenoid with maximum inductance and minimum volume as the objective functions. The proposed approach uses Local search, dominance principle and feasibility to identify solutions that deserve to be cloned.

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Section: Mathematical Problem Statement [1]mentioning

confidence: 99%

Section: Shape Design Of a Linear Synchronous Motormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems

El-Refaey¹

2017

AJNNA

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“…The MACO [4] algorithm is suggested to solve multiobjective optimization problems. The main intention of using MACO is to minimize the total cost of the power distribution systems.…”

Section: Introductionmentioning

confidence: 99%

A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

Sankaramoorthy¹,

Veeraraghavan²

2017

Turk J Elec Eng & Comp Sci

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This paper presents a multiobjective optimization methodology to optimally place a STATCOM in electric power distribution networks. The combination of multiobjective ant colony optimization (MACO) and the bacterial foraging optimization algorithm (BFOA) is proposed to minimize the power loss and total cost. The main intention of this analysis is to optimally place the STATCOM at multiple locations such as the transmission side, middle, and load side. Identifying the type and location of the STATCOM is a combinatorial optimization problem in power systems.In order to overcome this problem, the combination of hybrid MACO and BFOA algorithms is applied in this analysis to minimize the total cost and power loss. The total cost of the overall network is calculated by using system average interruption duration index and[ system average interruption frequency index metrics. Moreover, the BFOA is used in this paper to minimize the power loss during distribution, which is adequate in searching for the optimal solution.The problem of reducing power losses in distribution systems through the BFOA approach is described here for a 5-bus system. Test results of a 5-bus network show that the proposed MACO with BFOA method can efficiently ensure the power loss and total cost minimization.

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“…Many meta-heuristic paradigms such as genetic algorithm, simulated annealing, and tabu search have shown their efficacy in solving computationally intensive problems [6][7][8][9]. The studies on heuristic algorithms over the past few years have shown that these methods can be efficiently used to eliminate most of difficulties of classical methods.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

Galal¹,

Mousa²,

AL-Matrafi³

2013

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In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.

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A hybrid ant colony optimization approach based local search scheme for multiobjective design optimizations

Cited by 39 publications

References 13 publications

Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems

Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems

A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

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