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Area of Science:

  • Optimization
  • Artificial Intelligence
  • Computer Science

Background:

  • Dynamic optimization is crucial, yet research often favors continuous over combinatorial problems.
  • Existing algorithms struggle with dynamic combinatorial challenges.
  • Real-world problems frequently involve dynamic combinatorial optimization.

Purpose of the Study:

  • To propose a novel algorithm for dynamic combinatorial optimization.
  • To adapt the binary wolf pack algorithm (BWPA) for dynamic environments.
  • To evaluate the performance of the flexible binary wolf pack algorithm (FWPA).

Main Methods:

  • Developed a flexible binary wolf pack algorithm (FWPA) by integrating a flexible population updating strategy with BWPA.
  • Applied FWPA to static and dynamic multidimensional knapsack problems.
  • Compared FWPA against state-of-the-art algorithms and the basic BWPA.

Main Results:

  • FWPA demonstrated feasibility and competitiveness in dynamic optimization scenarios.
  • The proposed algorithm achieved strong performance on benchmark dynamic multidimensional knapsack problems.
  • This research is the first to investigate wolf pack algorithms on dynamic combinatorial problems.

Conclusions:

  • FWPA is a viable and effective approach for dynamic combinatorial optimization.
  • The flexible population updating strategy enhances algorithm performance in dynamic settings.
  • The findings suggest FWPA's potential for practical applications requiring dynamic optimization.