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

Yanhong Feng1, Gai-Ge Wang2, Qingjiang Feng3

  • 1School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China.

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Summary
This summary is machine-generated.

This study introduces a hybrid cuckoo search (CS) and improved shuffled frog-leaping algorithm (ISFLA) to solve the 0-1 knapsack problem effectively. The new algorithm demonstrates superior performance in finding high-quality solutions compared to existing methods.

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Area of Science:

  • Optimization Algorithms
  • Combinatorial Optimization
  • Computer Science

Background:

  • The 0-1 knapsack problem is a classic combinatorial optimization challenge.
  • Existing algorithms like binary cuckoo search, binary differential evolution, and genetic algorithms have limitations in solving this problem efficiently.

Purpose of the Study:

  • To develop an effective hybrid algorithm for the 0-1 knapsack problem.
  • To improve convergence speed and exploitation capabilities in optimization.

Main Methods:

  • A hybrid cuckoo search (CS) algorithm integrated with an improved shuffled frog-leaping algorithm (ISFLA).
  • Incorporation of global optimal information, inter-individual communication, and genetic mutation within the ISFLA framework.
  • A novel CS model leveraging Lévy flights and the frog-leap operator.
  • Application of a greedy transform method for solution repair and optimization.

Main Results:

  • Numerical simulations on six distinct 0-1 knapsack problem instances were conducted.
  • The proposed hybrid algorithm demonstrated effectiveness in achieving high-quality solutions.
  • Comparative analysis showed the algorithm outperformed binary cuckoo search, binary differential evolution, and genetic algorithms.

Conclusions:

  • The hybrid CS-ISFLA algorithm is a promising approach for solving the 0-1 knapsack problem.
  • The integration of ISFLA and CS enhances convergence and solution quality.
  • The greedy transform method effectively handles solution feasibility and optimization.