International Research Awards on Computer Aided Design in Mechanical Engineering

 

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability.

Introduction

The Optimization Problem (OP)1 as defined by refers to the task of identifying the optimal choice among various strategies and parameters under specific conditions. This problem is prevalent in real-world applications and encompasses a wide range of scenarios where the goal is to find the best solution within a set of alternatives. Some of the classic intelligent optimization algorithms, including Particle Swarm Optimization (PSO) inspired by bird foraging behaviors2,3, Genetic Algorithm (GA) simulating genetic and evolutionary processes4, Ant Colony Optimization (ACO)5 mimicking ant collective pathfinding, and Simulated Annealing (SA)6 emulating material annealing, have been widely applied in various fields. In recent years, researchers have introduced novel intelligent optimization algorithms for solving optimization problems. For instance, the Bat Algorithm (BA)7 is inspired by the echolocation behavior of bats in detecting prey and navigating around obstacles. The Grey Wolf Optimization Algorithm (GWO)8 draws inspiration from the leadership and hunting behavior of wolf packs. The Hybrid Frog-Leaping Algorithm (SFAL)9 is inspired by the foraging mechanisms of frogs in constrained environments. Additionally, the Moth Flame Optimization Algorithm (MFO)10 is based on the spiral flight behavior of moths around flames. These emerging algorithms have shown promise in addressing a wide range of optimization challenges. Different intelligent optimization algorithms continue to drive advancements and transformations in the industrial sector and real-world applications. For instance, scheduling problems11,12,13, industrial manufacturing14,15, aviation16,17, facial recognition18,19,20, and medical imaging21,22, among others, have all seen the influence and application of various intelligent optimization algorithms.

The Whale Optimization Algorithm (WOA)23 is a novel intelligent optimization algorithm proposed by Australian researchers in 2016. It is inspired by the collective hunting behavior of whales in the natural world. This algorithm offers advantages such as simplicity in principles, fewer parameters, and ease of implementation. It has successfully been applied to solve a variety of problems in fields such as image retrieval24, image segmentation25, medicine26, energy27, neural networks28, feature selection29, wind speed prediction30, key recognition31, and sentiment analysis32, among others. However, WOA still faces challenges when applied to nonlinear, high-dimensional, and complex optimization problems, including issues related to low optimization precision, slow convergence, and susceptibility to local convergence. To address these challenges, researchers have proposed various strategies to enhance WOA.

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