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This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a relatively new bio‐inspired meta‐heuristic optimization algorithm that mimics the navigating and foraging behavior of salps in oceans. This paper presents an orthogonal quasi‐opposition‐based learning‐driven dynamic SSA (OBDSSA) for solving global optimization problems. The proposed methodology integrates orthogonal quasi‐opposition‐based learning (OQOBL) tactic and dynamic learning (DL) strategy with SSA to improve its performance. The OQOBL technique is used to enrich the exploration and development capability of the canonical SSA to help the salp chain jump out of local optimum, while the DL mechanism is applied to the basic approach to expand the neighborhood searching capabilities of the search agents. To investigate the proposed operators and OBDSSA algorithm, 18 widely used benchmark functions and parameter extraction problem of photovoltaic (PV) model have been experimented upon. The comparisons reveal that OBDSSA outperforms all competitors, including the standard SSA, SSA variants, and other state‐of‐the‐art algorithms. Finally, the developed approach is applied to path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots (AMR) and satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems. [ABSTRACT FROM AUTHOR]