Hybrid ACO-CI Algorithm for Beam Design problems

TL;DR

This paper introduces a hybrid ACO-CI algorithm that efficiently solves complex optimization problems, demonstrating superior performance in benchmark tests and mechanical design applications with fewer iterations and reduced computational time.

Contribution

A novel hybrid ACO-CI algorithm combining sample space reduction with Ant colony optimization for improved efficiency in solving optimization problems.

Findings

Fewer iterations needed for convergence.

Outperforms existing algorithms in mechanical design problems.

Effective in benchmark function optimization.

Abstract

A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmark test functions. Furthermore, the constrained version of the algorithm is used to solve two mechanical design problems involving stepped cantilever beams and I-section beams. The effectiveness of the proposed technique of solution is evaluated relative to contemporary algorithmic approaches that are already in use. The results show that our proposed hybrid ACO-CI algorithm will take lesser number of iterations to produce the desired output which means lesser computational time. For the minimization of weight of stepped cantilever beam and deflection in I-section beam a proposed hybrid ACO-CI algorithm yielded best results when compared to other existing algorithms. The proposed work could be investigate for variegated real world applications encompassing domains of engineering, combinatorial and health care problems.