Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Abstract

To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm

“…In Equation (12), J is the objective vector, and the problem is a constrained minimization bi-objective optimization problem.…”

“…Step 3: Calculate the fitness value J of a tuna according to Equation (12), and determine whether the constraint conditions are met using Equation (13). Compare the fitness values that meet the constraint conditions, update the optimal value of the current individual tuna (i.e., the fitness value of the tuna), and save it.…”

“…Swarm intelligence optimization methods compensate for the shortcomings of conventional optimization approaches, enabling them to address complex real-world situations successfully [9][10][11]. As a result, more scholars are drawing inspiration for their own swarm intelligence optimization techniques from a variety of biological phenomena [12][13][14]. These algorithms are well known for their ease of use and efficacy in handling a broad variety of real-world applications.…”

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

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