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A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for

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This study introduces novel recombination and hypermutation operators for artificial immune systems. These enhancements improve clonal selection algorithms, addressing premature convergence and boosting accuracy in complex optimization tasks.

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Area of Science:

  • Artificial Intelligence
  • Computational Biology
  • Optimization Algorithms

Background:

  • Artificial immune systems (AIS) are bio-inspired computational methods.
  • Clonal selection algorithms (CSAs) are a prominent type of AIS.
  • Traditional CSAs struggle with complex optimization problems like multimodality and high dimensionality, often leading to premature convergence and reduced accuracy.

Purpose of the Study:

  • To enhance the performance of clonal selection algorithms (CSAs).
  • To address limitations of traditional CSAs, specifically premature convergence and accuracy issues.
  • To introduce novel operators inspired by biological immune mechanisms.

Main Methods:

  • Proposed a recombination operator inspired by biological combinatorial recombination.
  • Introduced a modified hypermutation operator.
  • Evaluated the proposed operators on 16 benchmark functions using comparative analysis.

Main Results:

  • The recombination operator enhances CSA search ability by fusing information from parent solutions.
  • The modified hypermutation operator generates more efficient candidate solutions.
  • Comparative studies demonstrated significant performance improvements over classic CSA, RCSA, and RHCSA.

Conclusions:

  • The proposed recombination and hypermutation operators effectively improve CSA performance.
  • The enhanced CSA (RHCSA) shows competitive results compared to state-of-the-art algorithms.
  • This work offers a more robust and accurate approach for complex optimization problems using AIS.