Distributed Influence Search for Engineering Optimization: Conditional Analysis and Reported Benchmarks

Authors

  • Bilal Ahmad Khan * Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan. https://orcid.org/0000-0001-6059-3985
  • Wolfgang H. Müller Department of Computer Science, Technical University of Munich, Munich, Germany.

https://doi.org/10.22105/opt.v3i3.118

Abstract

Distributed Influence Search (DIS) uses fitness-gated movement on a periodically rebuilt nearest-neighbor graph, a feedback rate based on fitness-weight entropy, and stagnation-triggered recovery. This paper specifies those mechanisms for constrained continuous search and separates them from the assumptions needed for convergence. A valid coverage argument requires a fixed objective, consistently evaluated elitist memory and a uniform conditional probability of reaching relevant sublevel sets; unbounded Gaussian and Cauchy proposals with ε-level acceptance do not establish these conditions by themselves. Reported benchmark, ablation and application summaries are retained for descriptive analysis. They show lower DIS means on several displayed function rows, a 40-dimensional advantage for L-SHADE in the reported rank summary, and different leaders across engineering tasks. Their inferential interpretation remains limited by unresolved evaluation counts, run-level outputs, interval calculations and application feasibility definitions. The 40- and 80-dimensional benchmark settings extend the official CEC 2022 dimensional protocol. The study therefore presents a conditional analysis and a corrected account of reported observations, without certifying global convergence, matched computational work or deployment feasibility.

Keywords:

Distributed influence search, Information diffusion, Metaheuristic optimization, Constrained continuous optimization, Swarm intelligence, Engineering design

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Published

2026-09-15

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Vol. 3 No. 3 (2026): Optimality

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How to Cite

Khan, B. A. ., & Müller, W. H. . (2026). Distributed Influence Search for Engineering Optimization: Conditional Analysis and Reported Benchmarks. Optimality, 3(3), 248-269. https://doi.org/10.22105/opt.v3i3.118

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