Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Abstract

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.Manuscript received….revised….accepted….

Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization

“…In our future work, we will further develop the KSP-EA to enhance its performance for solving more difficult MaTOPs. Besides, we will also extend the idea of preserving knowledge structure in solving the real-world MaTOPs with more challenging properties, such as multi-objective [46]- [48], large-scale [49], [50], and multimodal optimization [51]- [53].…”

Learning-Aided Evolution for Optimization

“…Therefore, in the future, we will not only enhance the LEO-based algorithms to well address the above issues, but also hope to extend the idea of knowledge learning to more research aspects of EC and real-world application problems, such as large-scale problems [25]- [63], multimodal problems [64][65], and multi-task problems [66] [67]. Moreover, the automatic optimization of the network architecture and parameter of the LEO learning system for different scenarios will be worthy of study [68].…”

Power Optimization for Mixed Polarity Reed–Muller Circuits Based on Multilevel Adaptive Memetic Algorithm

“…In the proposed framework, diferential evolution with linear population size reduction and semiparameter adaptation is used for global exploration, and its efectiveness is verifed for large-scale global optimization problems. Similarly, in [38], Wang et al conducted targeting experiments on specifc genes in candidate solutions to balance the excessive dimensions of DEO and get rid of the limitation of bottleneck dimensions. In [39], Li et al proposed a novel three-layer distributed diferential evolution framework with adaptive resource allocation for allocating ftness evaluation budget resources in multiple populations.…”