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A novel meta-heuristic algorithm based on candidate cooperation and competition.

Yue Cong1, Bingnan Yang2, Jie Wei3

  • 1Modern Business School, Zhejiang Agricultural Business College, Shaoxing, 312088, China.

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Summary
This summary is machine-generated.

The new Candidates Cooperative Competitive Algorithm (CCCA), inspired by human social behavior, effectively solves continuous optimization problems. It demonstrates superior performance and robustness, outperforming existing algorithms in benchmark tests.

Keywords:
Candidates cooperative competitive algorithmHuman social behaviorMeta-heuristic algorithmOptimization

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Human-Inspired Computing

Background:

  • Existing meta-heuristic algorithms often lack inspiration from human social behavior.
  • Current human-social-behavior-inspired algorithms struggle with premature convergence and local optima.

Purpose of the Study:

  • To introduce a novel meta-heuristic algorithm, the Candidates Cooperative Competitive Algorithm (CCCA), for continuous optimization problems.
  • To address the limitations of existing algorithms by incorporating unique human social behaviors.

Main Methods:

  • CCCA employs a two-stage approach: self-study and mutual influence among candidates.
  • Mutual influence includes cooperative behaviors (e.g., assistance, discussions) and competitive mechanisms (e.g., contests, elimination).
  • The algorithm was tested on 23 classical benchmark functions and compared against established algorithms like PSO, FA, and CSA.

Main Results:

  • CCCA achieved optimal solutions in 9 out of 23 test functions.
  • Demonstrated a strong ability to escape local optima in 7 unimodal and multimodal functions.
  • Statistical analysis confirmed significant performance improvements in over 90% of test cases, outperforming 8 compared algorithms.

Conclusions:

  • CCCA exhibits superior performance, robustness, and a strong ability to escape local optima compared to existing algorithms.
  • The algorithm's practical effectiveness is validated through successful application to the capacity allocation problem.
  • CCCA represents a significant advancement in human-inspired meta-heuristic optimization.