A multi-population differential evolution with best-random mutation strategy for large-scale global optimization

Dragonfly visual evolutionary neural network: A novel bionic optimizer with related LSGO and engineering design optimization

Biological visual systems intrinsically include multiple kinds of motion-sensitive neurons. Some of them have been successfully used to construct neural computational models for problem-specific engineering applications such as motion detection, object tracking, etc. Nevertheless, it remains unclear how these neurons' response mechanisms can be contributed to the topic of optimization. Hereby, the dragonfly's visual response mechanism is integrated with the inspiration of swarm evolution to develop a dragonfly visual evolutionary neural network for large-scale global optimization (LSGO) problems. Therein, a grayscale image input-based dragonfly visual neural network online outputs multiple global learning rates, and later, such learning rates guide a population evolution-like state update strategy to seek the global optimum. The comparative experiments show that the neural network is a competitive optimizer capable of effectively solving LSGO benchmark suites with 2000 dimensions per example and the design of an operational amplifier.

SaMDE: A Self Adaptive Choice of DNDE and SPIDE Algorithms with MRLDE

Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations.