Search

• A novel method to improve the performance of population-based optimizers. • The combination lock algorithm used as a pre-processing optimization method. • The combination lock algorithm produces an elite individual. • The elite individual is inserted into the initial population of optimizers. • The new algorithm significantly improves the performance of optimizers. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems. In this paper, a novel systematic pre-processing approach, called the combination lock algorithm, for obtaining a good starting point for population-based algorithms is proposed. The proposed algorithm is tested on 32 benchmark unconstrained multidimensional optimization problems of different characteristics that are either unimodal or multimodal, continuous or non-continuous, separable or non-separable, differentiable or non-differentiable. The tests also include four engineering constrained optimization benchmark problems. The experimental results of applying the proposed algorithm for the Particle Swarm Optimization, the Ant Colony Optimization for Continuous Domain, and the Grey Wolf Optimization were compared with the results obtained from the conventional approach of initializing the starting population of population-based metaheuristic methods. The simulation results show the potential of the proposed algorithm as an efficient and reliable approach to enhance the performance of population-based optimization algorithms such that, overall, 50% and up to 100% of the tested problems "across various population size" settings, had either improved or equalled the optimal values when the proposed algorithm was applied. [ABSTRACT FROM AUTHOR]

Abstract: In this study a novel optimization method is presented, the so called mine blast algorithm (MBA). The fundamental concepts and ideas of MBA are derived from the explosion of mine bombs in real world. The efficiency of the proposed optimizer is tested via the optimization of several truss structures with discrete variables and its performance is compared with several well-known metaheuristic algorithms. The results show that MBA is able to provide faster convergence rate and also manages to achieve better optimal solutions compared to other efficient optimizers. [Copyright &y& Elsevier]

• Hybrid water cycle with evaporation rate and Hookes-Jeeves algorithms. • The application of proposed hybrid method for data clustering problems. • The proposed method outperforms other clustering methods reported in literature. • Performance is based on solution quality and/or number of function evaluations. Cluster analysis is a valuable data analysis and data mining technique. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems including data clustering. In this paper, a recently proposed algorithm called the water cycle algorithm, based on the evaporation rate is used in conjunction with a local search method namely Hookes and Jeeves method to perform data clustering. Statistical analyses were carried out which show that the hybrid optimization method, in general, performs superior to the methods reported in the literature in terms of solution quality as well as computational performance. The proposed hybrid algorithm is tested on some selected standard datasets obtained from the UCI machine-learning repository. The objective function is based on the Euclidean distance as well as the DB index. The experimental results were compared with the data clustering results reported in published literature. The simulation results confirm the superiority of the proposed hybrid method as an efficient and reliable algorithm to solve clustering problems. [ABSTRACT FROM AUTHOR]