Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Abstract

As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm's convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm's update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

“…The techniques like diagonal linear uniform initialization can be used to reduce the computational cost (Li et al, 2021).…”

“…The process of population initialization also plays an important role in computational complexity, as selecting a large population may increase the complexity of the soft computing technique in predicting software maintainability. The techniques like diagonal linear uniform initialization can be used to reduce the computational cost (Li et al, 2021).…”

Metaheuristics: A Review of Algorithms

“…In practice, however, uniform distributions may not be suitable for all applications. There are also some other commonly used initialization techniques, such as chaotic initialization, sequencebased deterministic initialization, opposition-based learning, and Latin hypercube sampling [128].…”

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

“…For a recent approach to restrict the sampling to a subspace, see, for example, Li et al. (2021a). Watson (2010) asserted that the computational cost of sampling should be significantly lower than the cost of solving the problem with randomly generated individuals.…”

“…Additionally, we refer to Watson (2010) who pointed out that sampling the search space should attempt to provide as wide a coverage of the search space as possible within the limits of an acceptable computational cost. For a recent approach to restrict the sampling to a subspace, see, for example, Li et al (2021a). Watson (2010) asserted that the computational cost of sampling should be significantly lower than the cost of solving the problem with randomly generated individuals.…”