A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Abstract

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior

“…Yang et al [ 20 ] improved the light intensity pre-processing and calculation method based on the Plant Growth Algorithm (PGPP), resulting in a smoother planned path for UAS with their proposed Light-Sensitive Enhanced Plant Growth Algorithm (PEPG). Jiang et al [ 21 ] introduced a novel Golden Jackal Optimization Algorithm (SCMGJO) by combining the sine-cosine algorithm with the Cauchy mutation algorithm, enhancing the global exploration capability of UAVs, and improving the efficiency of path planning algorithms to obtain the optimal solutions. Chen et al [ 22 ] combined the Gray Wolf algorithm (GWO) with the artificial potential field method (APF) to propose a Fusion Optimization Algorithm (GGO-APF), which enhanced the stability, safety, and efficiency of UAVs’ path planning operations.…”

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

“…With the continuous development of science and technology, the complexity and scale of various practical application problems and optimization problems are increasing, and the traditional optimization problem-solving methods are no longer suitable for solving complex problems or make it difficult to meet the needs of high-precision solutions [1,2]. In recent years, swarm intelligence algorithms inspired by various biological groups in nature have been widely studied by international scholars, such as particle swarm optimization (PSO) [3], the butterfly optimization algorithm (BOA) [4], the SALP Swarm Algorithm (SALP) [5] and so on. This kind of algorithm has been widely used in solving optimization problems and other scientific fields because of its simple principle, high flexibility and high efficiency.…”