This paper shows the results obtained in the analysis of the impact of Distributed Generation (DG) on distribution losses and presents a new algorithm to the optimal allocation of distributed generation resources in distribution networks. The optimization is based on a Shuffled Frog Leaping Algorithm (SFLA) aiming to optimal DG allocation in distribution network. Shuffled frogleaping algorithm ...

This paper presents an effective shuffled frogleaping algorithm (SFLA) for solving a lot-streaming flow shop scheduling problem with equal-size sublots, where a criterion is to minimize maximum completion time (i.e., makespan) under both an idling and no-idling production cases. Unlike the original SFLA, the proposed SFLA represents an individual or frog as a job permutation and utilizes a posi...

This paper introduces a Shuffled Frog-Leaping Algorithm based method for the optimization of machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. The algorithm is compared with others techniques and outperforms the results reached by standard shuffled frogleaping algorithm, differential evolution, particle swarm optimizat...

Shuffled frog-leaping algorithm (SFLA) is a heuristic optimization technique based on swarm intelligence that is inspired by foraging behavior of the swarm of frogs. The traditional SFLA is easy to be premature convergence. So, we present an improved shuffled frog-leaping algorithm with single step search strategy and interactive learning rule(called ‘SI-SFLA’). Single step search strategy enha...

This paper proposes a study of quality of service (QoS) in cognitive radio networks. This study is based on a stochastic optimization method called shuffled frog leaping algorithm (SFLA). The interest of the SFLA algorithm is to guarantee a better solution in a multi-carrier context in order to satisfy the requirements of the secondary user (SU).

Shuffled frog-leaping algorithm (SFLA) has long been considered as new evolutionary algorithm of group evolution, and has a high computing performance and excellent ability for global search. Knapsack problem is a typical NP-complete problem. For the discrete search space, this paper presents the improved SFLA (ISFLA), and solves the knapsack problem by using the algorithm. Experimental results...

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evo...

In order to overcome the defects of Shuffled Frog Leaping Algorithm (SFLA) such as nonuniform initial population, slow searching speed in the late evolution and easily trapping into local extremum, a new Shuffled Frog Leaping Algorithm based on mutation operator and population diversity is proposed in this paper. The algorithm has overcome the disadvantage mentioned above. The algorithm efficie...

This paper proposes Hybrid SFL-Bees Algorithm that combines strengths of Shuffled Frog Leaping Algorithm (SFLA) and Bees Algorithms (BA). While SFLA can find optimal solutions quickly because of directive searching and exchange of information, BA has higher random that make it easily escape local optima to find global solutions. Thus Hybrid SFL-Bees Algorithm is able to find optimal solutions q...

This paper proposes Hybrid SFL-Bees Algorithm that combines strengths of Shuffled Frog Leaping Algorithm (SFLA) and Bees Algorithms (BA). While SFLA can find optimal solutions quickly because of directive searching and exchange of information, BA has higher random that make it easily escape local optima to find global solutions. Thus Hybrid SFL-Bees Algorithm is able to find optimal solutions q...