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To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and complex systems, many intelligent optimization methods have been proposed. Among these algorithms, the particle swarm optimization (PSO) algorithm has attracted scholars’ attention. However, the traditional PSO can easily become an individual optimal solution, leading to the transition of the optimization process from global exploration to local development. To solve this problem, in this paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on the theory of reinforcement learning in psychology. First, we used the reinforcement learning strategy to optimize the initial population in the population initialization stage; then, chaotic adaptive weights and adaptive learning factors were used to balance the global exploration and local development process, and the individual optimal solution and the global optimal solution were obtained using dimension learning. Finally, the improved reinforcement learning strategy and mutation strategy were applied to the traditional PSO to improve the quality of the individual optimal solution and the global optimal solution. The HRLPSO algorithm was tested by optimizing the solution of 12 benchmarks as well as the CEC2013 test suite, and the results show it can balance the individual learning ability and social learning ability, verifying its effectiveness.

Achieving mutualistic symbiosis is the core concept of China’s promotion of the Belt and Road Initiative (BRI). Therefore, how to guide China’s OFDI location decision-making in the Belt and Road (B&R) countries from the perspective of mutualistic symbiosis is of great significance. This paper constructs the B&R symbiotic system based on symbiotic system theory, to establish the investment suitability index from three dimensions: stability, coordination, and sustainability, then uses the entropy weight, TOPSIS, and GIS method to assess and analyze the investment suitability index of 60 major countries in B&R’s six economic corridors. The findings indicate that Russia, India, Indonesia, Thailand, Singapore, Vietnam, and Malaysia have the highest investment suitability and are China’s most favorable OFDI destinations for achieving mutualistic symbiosis. Conversely, Syria, Lebanon, Jordon, and Yemen have the lowest investment suitability. Based on these results, policymakers can formulate more scientific and reasonable policies to guide China’s OFDI to make location decisions in B&R countries with high investment suitability, so as to promote the high-quality and sustainable development of B&R construction. However, due to data acquisition and technical reasons, this study does not analyze investment suitability from time trends. The analysis of investment suitability from more B&R countries or different types of industries is an important direction for further research in the future.