KU battle of metaheuristic optimization algorithms 1: Development of six new/improved algorithms

Abstract

Each of six members of hydrosystem laboratory in Korea University (KU) invented either a new metaheuristic optimization algorithm or an improved version of some optimization methods as a class project for the fall semester 2014. The objective of the project was to help students understand the characteristics of metaheuristic optimization algorithms and invent an algorithm themselves focusing those regarding convergence, diversification, and intensification. Six newly developed/improved metaheuristic algorithms are Cancer Treatment Algorithm (CTA), Extraordinary Particle Swarm Optimization (EPSO), Improved Cluster HS (ICHS), Multi-Layered HS (MLHS), Sheep Shepherding Algorithm (SSA), and Vision Correction Algorithm (VCA). This paper describes the details of the six developed/improved algorithms. In a follow-up companion paper, the six algorithms are demonstrated and compared through well-known benchmark functions and a real-life engineering problem.