Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization

Eusuff, Muzaffar; Lansey, Kevin; Pasha, Fayzul

doi:10.1080/03052150500384759

Cited by 1,038 publications

(486 citation statements)

References 14 publications

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“…At present on the results of theoretical research of Shuffled frog leaping algorithm is not much and related research: Elbeltagi Emad compared the searching mechanism of the genetic algorithm and shuffled frog leaping algorithm in 2005, the experimental results show that the shuffled frog leaping algorithm in solving some problems of continuous functions have better performance than genetic algorithm [2]. Eusuff studied the system theory of basic shuffled frog leaping algorithm in 2006, the perfect shuffled frog leaping algorithm was used to solve typical combinatorial optimization problems [3]. Elbeltagi Emad proposed an improved shuffled frog leaping algorithm in 2007, proposed the parameter of accelerate search scope, analyzed the positive role of the new parameters, and solved discrete optimization problem and continuous optimization problem [4].…”

Section: Introductionmentioning

confidence: 99%

An improved Shuffled frog leaping Algorithm and its application

Mai¹,

Li²

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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Section: Introductionmentioning

confidence: 99%

An improved Shuffled frog leaping Algorithm and its application

Mai¹,

Li²

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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“…The SFLA is a novel meta-heuristic optimization method inspired from the natural memetic evolution of a group of frogs when searching for the location that has the maximum amount of available food [6]. The SFLA combines the advantages of both genetic-based memetic algorithms and social behavior based particle swarm optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] an improved genetic algorithm (IGA) with stochastic crossover technique and elitism are applied to solve the GEP problem. The results of the IGA are compared with those of the conventional; such as simple genetic algorithm, the full DP and the tunnel-constrained DP.The SFLA is a novel meta-heuristic optimization method inspired from the natural memetic evolution of a group of frogs when searching for the location that has the maximum amount of available food [6]. The SFLA combines the advantages of both genetic-based memetic algorithms and social behavior based particle swarm optimization algorithms.…”

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Application of Shuffled Frog Leaping Algorithm to Long Term Generation Expansion Planning

Jadidoleslam¹,

Bijami²,

Amiri³

et al. 2012

IJCEE

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Abstract-This paper presents the application of an efficient shuffled frog leaping algorithm (SFLA) to solve the optimal generation expansion planning (GEP) problem. The SFLA is a meta-heuristic search method inspired by natural memetics. It combines the advantages of both genetic-based memetic algorithms and social behavior based algorithm of particle swarm optimization. Least-cost GEP is concerned with a highly constrained non-linear discrete dynamic optimization problem. In this paper the proposed formulation of problem, determines the optimal investment plan for adding power plants over a planning horizon to meet the demand criteria, fuel mix ratio, and the reliability criteria. To test the proposed SFLA method, it is simulated for two test systems in a time horizon of 10 and 20 years respectively. The obtained results show that compared to the traditional methods, the SFLA method can provide better solutions for the GEP problem, especially for a longer time horizon.Index Terms-Generation expansion planning, probabilistic production simulation, shuffled frog leaping algorithm. I. INTRODUCTIONThe Generation Expansion Planning (GEP) is a problem to determine when, where, what type and how much capacity of new power plants should be constructed over a long-term planning horizon to meet forecasted demand according to a pre-specified reliability criteria. GEP is an important decision-making activity for utility companies. The main objective of GEP is to minimize the total investment, operating, and outage (energy-not-served) costs of power system.There are generally two deterministic and stochastic approaches to solve the GEP problem. The Stochastic approach takes into account uncertainties associated with the input data, such as forecasted demand, fuel prices, economic and technical characteristics of new evolving generating technologies, construction lead times, and governmental regulations [1]. The deterministic approach solves the problem under different scenarios. In this case a fast and efficient method is required because of great number of simulations needed to be run under different scenarios.Least-cost GEP is concerned with a highly constrained non-linear discrete dynamic optimization problem. The high non-linearity of a GEP problem originates from the nature of the production cost and the set of non-linear constraints. The dynamic programming (DP) approach is one of the most used Manuscript received February 20, 2012; revised March 16, 2012 algorithms in GEP. However in the generation expansion problem, due to high dimensionality, the DP is not an efficient method for real power systems. In commercial packages like WASP [2], to overcome this difficulty, heuristic tunnel-based techniques are used in DP routine, where users pre-specified configurations and successively modified tunnels are considered to arrive at local optimums.The emerging optimization techniques used to solve GEP problem were reviewed in [3]. In [4] the meta-heuristic techniques; such as Genetic Algorithm, Differential Evolution, Evolu...

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“…The SFLA has been designed as a meta-heuristic to perform an informed heuristic search using a heuristic function (any mathematical function) to seek a solution of combinatorial optimization problem [9]. In essence, it combines the benefits of the genetic-based MAs and the social behavior-based PSO algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Shuffled Frog Leaping Algorithm for Solving No_Idle Permutation Flow Shop Scheduling Problems

Wang

Bao

Chen

et al. 2011

AEF

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This paper presents a novel hybrid shuffled frog-leaping algorithm (HSFLA) for solving the no_idle permutation flow shop scheduling problems (NIFS) with the criterion to minimize the maximum completion time (makespan). First, the algorithm employs insert-neighborhood-based local search to enhance the searching ability. Second, it adopts roulette wheel selection operator to generate the global best frog in the early stage of the evolution which can expand the searching solution space. The experimental results show that the proposed algorithm is effective and efficient for different scale benchmarks of NIFS.

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Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization

Cited by 1,038 publications

References 14 publications

An improved Shuffled frog leaping Algorithm and its application

An improved Shuffled frog leaping Algorithm and its application

Application of Shuffled Frog Leaping Algorithm to Long Term Generation Expansion Planning

A Hybrid Shuffled Frog Leaping Algorithm for Solving No_Idle Permutation Flow Shop Scheduling Problems

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