An effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 0-1 knapsack problems.

Yanhong Feng,Qingjiang Feng,Xiang-Jun Zhao,Gai-Ge Wang

doi:10.1155/2014/857254

Abstract

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm.

Highlights

The application of nature-inspired metaheuristic algorithms to computational optimization is a growing trend [1]
Comparisons of the best profits obtained from the CS algorithm with improved SFLA (CSISFLA) with those obtained from genetic algorithm (GA), differential evolution (DE), and cuckoo search (CS) for six KP instances with 1200 items are shown in Figures 8, 9, 10, 11, 12, and 13
We proposed a novel hybrid cuckoo search algorithm with improved shuffled frog-leaping algorithm, called CSISFLA, for solving 0-1 knapsack problems

Summary

Introduction

The application of nature-inspired metaheuristic algorithms to computational optimization is a growing trend [1]. Many metaheuristic algorithms have been applied to solve knapsack problems, such as evolutionary algorithms (EA) [21], HS [22], chemical reaction optimization (CRO) [23], cuckoo search (CS) [24,25,26], and shuffled frog-leaping algorithm (SFLA) [27]. In 2003, Eusuff and Lansey firstly proposed a novel metaheuristic optimization method: SFLA, which mimics a group of frogs to search for the location that has the maximum amount of available food. Due to the distinguished benefit of its fast convergence speed, SFLA has been successfully applied to handle many complicated optimization problems, such as water resource distribution [29], function optimization [30], and resource-constrained project scheduling problem [31]

Methods

Results

Conclusion