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This paper proposes an improved memetic algorithm relying on ring neighborhood topology for constrained optimization problems based on our previous work in Cai et al. (Soft Comput (in press), ). The main motivation of using ring neighborhood topology is to provide a good balance between effective exploration and efficient exploitation, which is a very important design issue for memetic algorithms. More specifically, a novel variant of invasive weed optimization (IWO) as the local refinement procedure is proposed in this paper. The proposed IWO variant adopts a neighborhood-based dispersal operator to achieve more fine-grained local search through the estimation of neighborhood fitness information relying on the ring neighborhood topology. Furthermore, a modified version of differential evolution (DE), known as 'DE/current-to-best/1', is integrated into the improved memetic algorithm with the aim of providing a more effective exploration. Performance of the improved memetic algorithm has been comprehensively tested on 13 well-known benchmark test functions and four engineering constrained optimization problems. The experimental results show that the improved memetic algorithm obtains greater competitiveness when compared with the original memetic approach Cai et al. in (Soft Comput (in press), ) and other state-of-the-art algorithms. The effectiveness of the modification of each component in the proposed approach is also discussed in the paper. [ABSTRACT FROM AUTHOR]

The constrained optimization problem is widely used in real-world applications and always hard to handle when the objective function is multimodal or the constraints are too stringent. In this manuscript, an improved differential evolution algorithm is proposed by using a novel individual evaluation scheme as well as a designed constraint handling technique. Firstly, the constrained optimization problem is converted into a biobjective optimization model in which all constraints are taken as an integrated function and further optimized just like the original objective. Then, based on the present individuals, both a reference point and a dynamic line are generated. The distances from any individual to the reference point as well as the dynamic line are adopted to evaluate the individual, and used to categorize individuals for evolving into groups. In addition, in order to improve the feasibility of individuals, a novel constraint handling technique is presented by utilizing the locations of some infeasible points. Finally, the proposed algorithm is executed on some recent benchmark functions as well as the system reliability redundancy allocation problems, and the computation results show the effectiveness of these presented techniques. [ABSTRACT FROM AUTHOR]