Bat Algorithm With Generalized Fly for Combinatorial Production Optimization Problems

Dekhici, Latifa; Guerraiche, Khaled; Belkadi, Khaled

doi:10.4018/978-1-5225-6164-4.ch003

Cited by 2 publications

(1 citation statement)

References 64 publications

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“…A bat algorithm with generalized flight (BAG) is a hybridization of the bat algorithm and the GEWA algorithm. It was first introduced by [37] and then applied to manufacturing system scheduling in [38], and to a green economic power dispatch problem in [39]. Our improvement lies in adding a global search function to the classical BA, which is the global flight of the worst bats.…”

Section: Redundancy Optimization Methodsmentioning

confidence: 99%

Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

Guerraiche¹,

Dekhici

Châtelet

et al. 2021

Energies

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The design of energy systems is very important in order to reduce operating costs and guarantee the reliability of a system. This paper proposes a new algorithm to solve the design problem of optimal multi-objective redundancy of series-parallel power systems. The chosen algorithm is based on the hybridization of two metaheuristics, which are the bat algorithm (BA) and the generalized evolutionary walk algorithm (GEWA), also called BAG (bat algorithm with generalized flight). The approach is combined with the Ushakov method, the universal moment generating function (UMGF), to evaluate the reliability of the multi-state series-parallel system. The multi-objective design aims to minimize the design cost, and to maximize the reliability and the performance of the electric power generation system from solar and gas generators by taking into account the reliability indices. Power subsystem devices are labeled according to their reliabilities, costs and performances. Reliability hangs on an operational system, and implies likewise satisfying customer demand, so it depends on the amassed batch curve. Two different design allocation problems, commonly found in power systems planning, are solved to show the performance of the algorithm. The first is a bi-objective formulation that corresponds to the minimization of system investment cost and maximization of system availability. In the second, the multi-objective formulation seeks to maximize system availability, minimize system investment cost, and maximize the capacity of the system.

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Section: Redundancy Optimization Methodsmentioning

confidence: 99%

Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

Guerraiche¹,

Dekhici

Châtelet

et al. 2021

Energies

Self Cite

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Reliability Optimization of Wind Farm Power Systems Using Bat Algorithm with Generalized Flight

Guerraiche¹,

Châtelet

Dekhici

et al. 2020

Int. J. Rel. Qual. Saf. Eng.

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In this paper, two metaheuristics, namely the Bat algorithm (BAT) and a recent hybridization of bat algorithm with generalized walk evolutionary algorithm are presented. The bat algorithm and the Bat algorithm with generalized flight (BAG) are used to solve the problem of optimal redundancy design of series-parallel electrical systems. In order to assess the reliability of the heterogeneous system of multi-state series-parallel, the Ushakov method a universal moment generating function (UMGF) is used to allow fast estimation. The design objective is to maximize the reliability of the bulk power generation system from wind farm. The components of each electrical subsystem are characterized by their performance (capacity), cost and reliability. Reliability is defined as the ability to satisfy consumer demand that is given by a cumulative load curve. By comparing three algorithms cited in this study, the results show that the hybridization of Bat and Generalized Evolutionary Walk Algorithm (BAG) are effective in solving the reliability redundancy optimization problem (RROP). Two illustrative examples are presented.

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Bat Algorithm With Generalized Fly for Combinatorial Production Optimization Problems

Cited by 2 publications

References 64 publications

Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

Reliability Optimization of Wind Farm Power Systems Using Bat Algorithm with Generalized Flight

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