Publications

2025

1. Cluster Comput.

   Escape after love: Philoponella prominens optimizer and its application to 3D path planning

   Yuansheng Gao^†, Jinpeng Wang, and Changlin Li

   Cluster Computing, 2025

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   According to the No Free Lunch theorem, we need to propose new metaheuristic algorithms with different optimization behaviors to better solve global optimization problems. Therefore, this paper presents a novel metaheuristic called Philoponella prominens Optimizer (PPO). It is inspired by the special mating behavior of the P. prominens, and simulates escape, sexual cannibalism and predation behaviors of males after mating. The superiority of the PPO is verified by comparing with seven recently proposed metaheuristics on CEC2017 benchmark functions. Furthermore, the Wilcoxon signed-rank sum test confirms significance in 93.60%, the Friedman ranking test places the PPO first, and the Kruskal–Wallis test verifies that the PPO’s values is smallest on about 2/3 functions. Further tests on CEC2022 benchmark functions demonstrates PPO’s competitiveness against six state-of-the-art metaheuristics. Additionally, the PPO is applied to four engineering problems and 3D UAV path planning problem, where it consistently outperforms competitors.

2. Appl. Math. Model.

   Logistic-Gauss Circle optimizer: Theory and applications

   Jinpeng Wang, Yuansheng Gao^†, Lang Qin, and 1 more author

   Applied Mathematical Modelling, 2025

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   Chaotic maps can be used to make the distribution of the initial population more uniform, which improves the spatial exploration rate. Considering these advantages, this paper attempts to design search operations based on chaotic maps and develop a novel metaheuristic algorithm called the Logistic-Gauss Circle optimizer. The algorithm reasonably combines and reformulates the Logistic and Gauss maps into Logistic-Gauss search (exploration); reformulates the Circle map into Circle search (exploitation). Through these two operations, the proposed algorithm achieves global optimization. The performance of the proposed algorithm is validated by a comparative analysis with 5 high-quality metaheuristic algorithms on 10 benchmark functions. The results of statistical analyses, including the Wilcoxon signed-rank test and the Friedman test, indicate that the proposed algorithm outperforms its competitors. Furthermore, the strong competitiveness of the algorithm is verified through comparisons with 4 state-of-the-art algorithms. Finally, the proposed algorithm is applied to 5 real-world problems, thereby demonstrating its capability to address engineering optimization problems.

3. Cluster Comput.

   Freedom from inspiration! Achieving efficient metaheuristic optimization with delta plus

   Yuansheng Gao^†, Jinpeng Wang, Lang Qin, and 2 more authors

   Cluster Computing, 2025

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   Many metaheuristic algorithms rely too heavily on metaphorical sources of inspiration, resulting in unnecessary complexity and redundant operations. This contradicts the core goal of algorithm design: simplicity and efficiency. This paper attempts to break free from the constraints of inspiration and presents a simple yet efficient metaheuristic algorithm: Delta Plus. Specifically, we propose a Delta operation to drive the Delta Plus for global optimization. The key features of the Delta operation are: (1) it does not reliance on the best solution to guide the search, and (2) it considers both the current and previous iteration’s solution changes. We evaluate the Delta Plus and seven recent metaheuristics on the CEC2017 benchmark, as well as the Delta Plus and five state-of-the-art algorithms on the CEC2022 benchmarks. Finally, the Delta Plus and seven recent metaheuristics are applied to solve five mechanical engineering problems. The results indicate that the proposed Delta Plus can be considered a simple yet efficient metaheuristic algorithm.

2024

1. J. Supercomput.

   Love evolution algorithm: A stimulus–value–role theory-inspired evolutionary algorithm for global optimization 🏆 Highly Cited Paper

   Yuansheng Gao^†, Jiahui Zhang, Yulin Wang, and 2 more authors

   The Journal of Supercomputing, 2024

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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.

2023

1. ESWA

   PID-based search algorithm: a novel metaheuristic algorithm based on PID algorithm 🏆 Highly Cited Paper

   Yuansheng Gao^†

   Expert Systems With Applications, 2023

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   In this paper, a metaheuristic algorithm called PID-based search algorithm (PSA) is proposed for global optimization. The algorithm is based on an incremental PID algorithm that converges the entire population to an optimal state by continuously adjusting the system deviations. PSA is mathematically modeled and implemented to achieve optimization in a wide range of search spaces. PSA is used to solve CEC2017 benchmark test functions and six constrained problems. The optimization performance of PSA is verified by comparing it with seven metaheuristics proposed in recent years. The Kruskal-Wallis, Holm and Friedman tests verified the superiority of PSA in terms of statistical significance. The results show that PSA can be better balanced exploration and exploitation with strong optimization capability.

2022

1. Front. Energy Res.

   An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

   Yuansheng Gao^†, Changlin Li, and Lei Huang

   Frontiers in Energy Research, 2022

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   To aim at the problem of inaccurate prediction of the remaining useful life of the lithium-ion battery, an improved grey wolf optimizer optimizes the deep extreme learning machine (CGWO-DELM) data-driven forecasting method is proposed. This method uses the grey wolf optimization algorithm based on an adaptive normal cloud model to optimize the bias of the deep extreme learning machine, the weight of the input layer, the selection of activation function, and the number of hidden layer nodes. In this article, indirect health factors that can characterize the degradation of battery performance are extracted from the discharge process, and the correlation between them and capacity is analyzed using the Pearson coefficient and Kendel coefficient. Then, the CGWO-DELM prediction model is constructed to predict the capacitance of the lithium-ion battery. The remaining useful life of lithium-ion batteries is indirectly predicted with a 1.44 A·h failure threshold. The prediction results are compared with deep extreme learning machines, long-term memory, other prediction methods, and the current public prediction methods. The results show that the CGWO-DELM prediction method can more accurately predict the remaining useful life of lithium-ion batteries.