A new index‐based hyper‐heuristic algorithm for global optimisation problems - Hasanzadeh, Mohammad Reza; Keynia, Farshid; Hashemipour, Maliheh (IET software, 2022)
In this research study, a new combination search algorithm, based on indexing its constituent processes, is proposed to solve global optimisation problems. As optimisation problems become more complex, especially real‐world problems, the use of higher‐performance algorithms has become essential. One of the techniques that lead to design of an algorithm with stronger strategies in exploration and exploitation, as well as a better balance between these two strategies, is the appropriate combination of parent algorithm processes. The proposed algorithm was developed using a new innovation with the help of the supply‐demand‐based optimisation and the Harris hawks optimisation algorithms processes as parent algorithms. In this algorithm, the local and global search sections of its parent algorithms, are separated, and then based on a new indexing method in each iteration, according to the current population indexing, a global search and a local search are selected from the processes of its parent algorithms, and then the current population is updated with two selected sections. The performance and effectiveness of the proposed algorithm in solving well‐known standard benchmark problems and in solving real‐world engineering problems have been tested and validated by statistical tools. The results of the research study show that the proposed algorithm can provide very effective results compared to other competing algorithms as well as its parent algorithms in many tests. The results show that the proper combination of optimisation algorithm processes can be used as a technique to design more powerful algorithms to solve global optimisation problems, especially complex real‐world problems.