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An efficient optimizer for the 0/1 knapsack problem using group counseling.

Yazeed Yasin Ghadi1, Tamara AlShloul2, Zahid Iqbal Nezami3

  • 1Department of Computer Science/Software Engineering, Al Ain University, Al Ain, UAE.

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Summary
This summary is machine-generated.

Group Counseling Optimizer (GCO), an evolutionary algorithm, efficiently solves the 0/1 knapsack problem. This approach offers a viable alternative to traditional methods like dynamic programming for optimization tasks.

Keywords:
CombinatorialEvolutionary algorithmEvolutionary algorithmxGCOKnapsackMachine learningOptimization

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Area of Science:

  • Computational Intelligence and Optimization
  • Operations Research

Background:

  • Optimization seeks to find the best solution by maximizing benefits and minimizing losses.
  • The 0/1 knapsack problem is a combinatorial challenge involving item selection to maximize value within weight constraints.
  • Dynamic programming optimally solves the knapsack problem but has a time complexity of O(n³).

Purpose of the Study:

  • To analyze the parameters of the Group Counseling Optimizer (GCO).
  • To apply GCO to solve the 0/1 knapsack problem (KP).
  • To evaluate GCO as an efficient alternative for solving combinatorial optimization problems.

Main Methods:

  • Feature analysis of Group Counseling Optimizer (GCO) parameters.
  • Implementation of GCO for solving the 0/1 knapsack problem.
  • Comparative evaluation of GCO's performance against existing methods.

Main Results:

  • The GCO-based approach demonstrated efficiency in solving the 0/1 knapsack problem.
  • Parameter analysis provided insights into GCO's behavior for optimization tasks.
  • GCO proved to be a competitive method for tackling combinatorial optimization challenges.

Conclusions:

  • The Group Counseling Optimizer (GCO) is an effective algorithm for the 0/1 knapsack problem.
  • GCO presents a viable and efficient alternative to traditional optimization techniques.
  • Further research into GCO parameter tuning can enhance its applicability in various optimization domains.