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A multiobjective human evolutionary optimization algorithm for complex engineering problems
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  • Published: 08 January 2026

A multiobjective human evolutionary optimization algorithm for complex engineering problems

  • D. Tarunika1 na1 &
  • Ashish Sharma1 na1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

Multi-objective optimization problems (MOPs) demand algorithms that effectively balance convergence, diversity, and computational efficiency. To address this challenge, a novel Multi-Objective Human Evolutionary Optimization Algorithm (MOHEOA) is proposed, inspired by the dynamics of human societal evolution. MOHEOA structures the search process into two adaptive phases: human exploration and human development, integrating a fixed-size dynamic archive to maintain and utilize non-dominated Pareto solutions. The algorithm begins with a logistic chaos mapping for population initialization, ensuring robust diversity. During the development phase, individuals are classified into leaders, explorers, followers, and losers, each employing specialized strategies tailored for multi-objective search. A roulette-wheel selection mechanism dynamically selects leaders from the archive, optimizing the trade-off between exploration and exploitation. To validate MOHEOA’s performance, extensive experiments on twenty-three benchmark test functions and four real-world engineering design problems are conducted. Comparative evaluations against state-of-the-art multi-objective algorithms demonstrate that MOHEOA consistently outperforms competitors in convergence speed, solution diversity, and Pareto optimality. The algorithm’s robustness and adaptability make it a compelling choice for complex optimization tasks. For reproducibility and further research, the MATLAB implementation of MOHEOA is publicly available at: https://github.com/swatzash/MOHEOA.

Data availability

The Code and datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Funding

Open access funding provided by Manipal Academy of Higher Education, Manipal. , India.

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Author notes

  1. D. Tarunika and Ashish Sharma contributed equally to this work.

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    D. Tarunika & Ashish Sharma

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  1. D. Tarunika
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  2. Ashish Sharma
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Tarunika D: Visualization & writing original draft; Ashish Sharma : Methodology, Validation.

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Correspondence to Ashish Sharma.

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Tarunika, D., Sharma, A. A multiobjective human evolutionary optimization algorithm for complex engineering problems. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34467-5

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  • Received: 16 August 2025

  • Accepted: 29 December 2025

  • Published: 08 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-34467-5

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Keywords

  • Dynamic archive
  • Human evolutionary dynamics
  • Multi-objective optimization
  • Pareto optimality
  • Roulette–Wheel Selection
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