A modification of tree-seed algorithm using Deb’s rules for constrained optimization

Abstract

This study focuses on the modification of Tree-Seed Algorithm (TSA) to solve constrained optimization problem. TSA, which is one of the population-based iterative search algorithms, has been developed by inspiration of the relations between trees and seeds grown on a land, and the basic version of TSA has been first used to solve unconstrained optimization problems. In this study, the basic algorithmic process of TSA is modified by using Deb’s rules to solve constrained optimization problems. Deb’s rules are based on the objective function and violation of constraints and it is used to select the trees and seeds that will survive in next iterations. The performance of the algorithm is analyzed under different conditions of control parameters of the proposed algorithm, CTSA for short, and well-known 13 constrained maximization or minimization standard benchmark functions and engineering design optimization problems are employed. The results obtained by the CTSA are compared with the results of particle swarm optimization (PSO), artificial bee colony algorithm (ABC), genetic algorithm (GA) and differential evolution (DE) algorithm on the standard benchmark problems. The results of state-of-art methods are also compared with the proposed algorithm on engineering design optimization problems. The experimental analysis and results show that the proposed method produces promising and comparable results for the constrained optimization benchmark set in terms of solution quality and robustness.