Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy

Abstract

Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local optimum. In addition, enlightened by differential evolution (DE) algorithm, this article proposes a novel evolutionary equation based on the fact that the disadvantaged group will search just around the best individual chosen from previous iteration to enhance the ability of local search. Simulation experiments based on a set of 18 benchmark functions show that, in general, COBSA displays obvious superiority in convergence speed and convergence precision when compared with BSA and the comparison algorithms.

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

“…COBSA's improvement is achieved through the use of the control factor of the population to the equation of variation. The experiments conducted in [29] indicated that COBSA improved the standard BSA in terms of convergence speed and precision. However, it showed low convergence precision in a few benchmark functions in the experiment compared to other EAs such as ABC and modified ABC (MABC).…”

“…The proposed technique led to a faster search than the standard BSA. Zhao et al proposed COBSA based on the standard BSA for overcoming the inefficiency of convergence precision and speed in the standard BSA [29]. COBSA's improvement is achieved through the use of the control factor of the population to the equation of variation.…”

“…3) In the literature, it is stated that BSA could be improved further [29,35,60,68,71,75,76,82]. The improvements can be related to efficiency, effectiveness, computing time, speed of convergence, and improvement of local and global searches.…”

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

“…As a result, many improvement research studies have been developed to overcome the drawback. e major modified research studies include the following five categories: (1) modified BSA algorithms by developing hybridization mechanisms [31][32][33][34][35][36][37][38][39], (2) modified BSA algorithms by developing novel initialization mechanisms [40][41][42][43][44][45][46], (3) modified BSA algorithms by developing novel reproduction (mutation or crossover) mechanisms [47][48][49][50][51][52][53][54][55], (4) modified BSA algorithms by developing novel selection mechanisms [56][57][58][59][60], and (5) modified BSA algorithms by developing novel control mechanisms for parameters (including the control factor F which is randomly generated in the original BSA) [61][62][63][64][65][66][67][68][69].…”

“…And two years later, Chen et al [53] proposed a new mutation operator with knowledge learning and adaptive control parameter. Zhao et al [54] introduced population control factor and optimal learning strategy into the mutation process of the BSA to improve the performance of algorithm. Yu et al [55] proposed a multiple learning strategy to replace reproduction mechanism of the original BSA.…”