A Multi-Strategy Improved Love Evolution Algorithm for Global Optimization Problems and Real-World Problems

This paper proposes a Multi-strategy Improved Love Evolution Algorithm, named MSILEA, to overcome the limitations of the original Love Evolution Algorithm (LEA) in complex optimization tasks. Although LEA has a distinctive stimulus–value–role interaction mechanism, its linear search-radius control, distance-dominated behavioral decision rule, and weak directional learning in the value phase make it prone to insufficient exploitation, ineffective behavioral switching, and local optimum trapping on rotated, hybrid, and composition functions. To address these issues, MSILEA introduces three complementary strategies: a nonlinear two-stage search radius regulation strategy, a quality–distance joint decision strategy, and a winner-direction differential learning strategy. These strategies respectively improve stage-dependent search control, multi-criteria behavioral selection, and directional learning ability. From the perspective of the symmetry concept, the proposed MSILEA can be regarded as an optimization framework that dynamically regulates the symmetry and asymmetry of population interactions. The encounter and role mechanisms preserve paired interaction symmetry among candidate solutions, whereas the quality–distance joint decision and winner-direction differential learning strategies introduce controlled symmetry breaking to guide the population toward higher-quality regions of the search space. MSILEA is evaluated on the CEC2017 and CEC2022 benchmark suites and compared with nine representative classical and advanced metaheuristic algorithms. On the 30-dimensional CEC2017 suite, MSILEA achieves the best Friedman mean rank of 1.93, outperforming the original LEA with a mean rank of 4.60. On the CEC2022 suite, MSILEA also obtains the best mean ranks of 2.50 and 2.00 in the 10-dimensional and 20-dimensional cases, respectively. In the microgrid day-ahead optimal scheduling problem, MSILEA obtains the lowest mean operating cost of 1.23 × 106 CNY and reduces the cost by approximately 50.80% compared with LEA. The average CPU time of MSILEA is 18.47 s, which is comparable to LEA and lower than several improved competitors. These results indicate that MSILEA can improve optimization accuracy, convergence robustness, and engineering feasibility without increasing the theoretical computational complexity.