0-1 knapsack problem is a typical NP complete problem. Shuffled frog leaping algorithm is applied successfully in many combinational optimization problems. Therefore, the paper introduces an improved shuffled frog leaping algorithm for solving 0-1 knapsack problem. It greatly reduces the searching time of shuffled frog leaping algorithm. It also effectively ameliorates the disadvantage of easil...

    Hub covering location problem, Network design,   Single machine scheduling, Genetic algorithm,   Shuffled frog leaping algorithm   Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc....

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffl...

In order to overcome the drawbacks of standard shuffled frog leaping algorithm that converges slowly at the last stage and easily falls into local minima, this paper proposed two-phases learning shuffled frog leaping algorithm. The modified algorithm added the elite Gaussian learning strategy in the global information exchange phase, updated frog leaping rule and added the learning capability t...

The parameter identification problem can be modeled as a non-linear optimization problem. In this problem, some unknown parameters of a mathematical model presented by an ordinary differential equation using some experimental data must be estimated. This paper presents a shuffled frog leaping algorithm for solving parameter identification problem. An opposition-based initialization strategy is ...

Resource scheduling under the condition of cloud computing has always been the focus of current research. This paper analyzes the current situation of cloud computing and introduces shuffled frog leaping algorithm in resource allocation. Aiming at that shuffled frog algorithm is easy to fall into local optimum with fast convergence speed, artificial vector machine is introduced into the subgrou...

In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two metaheuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hy...

--------------------------------------------------------ABSTRACT----------------------------------------------------------To enhance the optimization ability of classical shuffled frog leaping algorithm, a quantum inspired shuffled frog leaping algorithm with adaptive grouping is proposed. In this work, the frog swarms are adaptive grouped according to the average value of the objective functio...

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the m...

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...