An Evolutionary Hill Climbing Algorithm for Dynamic Optimisation Problems

1 Introduction In contrast to stationary problems where the problem parameters are static, dynamic optimisation problems (DOPs) present a challenging research area to the community [1]. This is mainly because, changes can occur any time during the optimisation course. Hence, when such a change happens (for example a change in the fitness landscape), the previous local optimal solution is no longer be as it is [2,3]. It is widely known that local search algorithms usually face a serious challenge for dynamic optimisation problems because they operate on a single solution which can easily get stuck in a local optima [2]. In order to overcome this shortage, local search algorithms should be restarted from scratch. Although, it seems a reasonable solution, restarting the algorithm from scratch will be time consuming as it may lose some promising areas. A promising trend to solve this issue is to hybridise a local search with a population-based method such as a genetic algorithm [2] [3]. Literature shows that population-based methods have been widely and successfully used to tackle dynamic optimisation problems (DOPs). The work in this paper is motivated by the success of the above mentioned methods. Thus, we propose the evolutionary hill climbing algorithm which combines the population of solutions with the hill climbing local search for dynamic optimisation problems. Our aim here is to use a population of solutions (instead of a single solution) in order to keep track of any changes during the search process, since the population of solution are scattered over the entire search space and consequently will increase the diversification of the algorithm. A hill climbing algorithm is applied to refine the promising solution generated by the population method. Once the solution cannot be improved for a certain number of iterations, then the hill climbing algorithm will restart the search by selecting a new solution from the population (instead of generating a new one). This idea will not only reduce the computation time, but will also help the search to focus on a promising point, as they already been improved previously and been modified by the population-based method. The performance of the proposed method is validated over the well-known dynamic optimisation functions i.e., OneMax, Plateau, Royal Road and Deceptive. Experimental results show that the proposed algorithm is able to achieve competitive results when compared to other available methods. The rest of this paper is outlined as follows: Section 2 …