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A boosted chimp optimizer for numerical and engineering design optimization challenges.

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Summary
This summary is machine-generated.

This study introduces Boosted Chimp Optimizer Algorithms to overcome limitations in the original Chimp Optimization Algorithm (ChoA). These enhanced algorithms demonstrate superior performance in solving complex optimization problems compared to existing methods.

Keywords:
CEC2005Engineering optimizationHybrid search algorithmsMeta-heuristics search

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The Chimp Optimization Algorithm (ChoA) is inspired by chimp behavior for global search.
  • Classical ChoA suffers from poor convergence and local minima entrapment in high-dimensional problems.

Purpose of the Study:

  • To develop improved variants of the Chimp Optimization Algorithm (ChoA).
  • To enhance both exploration and exploitation capabilities of the ChoA.
  • To validate the performance of the proposed algorithms on benchmark and engineering problems.

Main Methods:

  • Proposed two variants: IChoA-SHO (combining ChoA with SHO for exploration) and another variant for exploitation enhancement.
  • Tested and validated on standard uni-modal, multi-modal, and fixed-dimension benchmark functions.
  • Evaluated on 11 types of multidisciplinary engineering design problems.

Main Results:

  • The proposed Boosted Chimp Optimizer Algorithms showed improved convergence speed.
  • The enhanced variants effectively searched for both local and global optimal solutions.
  • Comparative analysis indicated superior performance over existing optimization methods.

Conclusions:

  • The developed Boosted Chimp Optimizer Algorithms offer significant improvements over the classical ChoA.
  • These enhanced algorithms are effective for tackling complex, high-dimensional optimization tasks.
  • The proposed methods present a promising advancement in the field of optimization algorithms.