An improved termite life cycle optimizer algorithm for global function optimization.

Abstract

The termite life cycle optimizer algorithm (TLCO) is a new bionic meta-heuristic algorithm that emulates the natural behavior of termites in their natural habitat. This work presents an improved TLCO (ITLCO) to increase the speed and accuracy of convergence. A novel strategy for worker generation is established to enhance communication between individuals in the worker population and termite population. This strategy would prevent the original worker generation strategy from effectively balancing algorithm convergence and population diversity to reduce the risk of the algorithm in reaching a local optimum. A novel soldier generation strategy is proposed, which incorporates a step factor that adheres to the principles of evolution to further enhance the algorithm's convergence speed. Furthermore, a novel replacement update mechanism is executed when the new individual is of lower quality than the original individual. This mechanism ensures a balance between the convergence of the algorithm and the diversity of the population. The findings from CEC2013, CEC2019, and CEC2020 test sets indicate that ITLCO exhibits notable benefits regarding convergence speed, accuracy, and stability in comparison with the basic TLCO algorithm and the four most exceptional meta-heuristic algorithms thus far.