An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems

Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.

Research of service selection algorithm for net-centric simulation task community

Being a new-generation C4ISR system simulation method,the construction approach of net-centric simulation(NCS)is developing toward net-centric from the traditional approach of platform-centric.NCS is mainly completed by the construction of the simulation task community(STC),the key to which being the dynamic integration of the various services spread in the network in order to form a new STC that meets the requirements of different users.In this study,a simulation task community service selection algorithm(STCSSA)is proposed.The main idea of this algorithm is to transform the construction of STC to the searching of optimal multi-objectives services with QoS global constraints.This paper first introduces the QoS model of STC and evaluates the service composition process,then presents the detailed operating process of STCSSA and design of the dynamic inertia weight strategy of the algorithm,and also proposes an optional variation method.Comparative tests were performed on STCSSA with other particle swarm optimization algorithms.It was validated from the perspective of performance that the proposed algorithm has advantages in improving the rate of convergence and avoiding local optimum,and from the perspective of practical application STCSSA also demonstrated feasibility in the construction of large-scale NCS task community.