Novel Artificial Human Optimization Field Algorithms – The Beginning

Abstract

New Artificial Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Behavior, Cognition and Thinking etc. But there are no Hybrid PSO articles which are based on concepts like Disease, Kindness and Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Optimization Field Algorithms. A total of 6 Artificial Optimization Field algorithms titled Human Safety Particle Swarm Optimization (HuSaPSO), “Human Kindness Particle Swarm Optimization (HKPSO), “Human Relaxation Particle Swarm Optimization (HRPSO), “Multiple Strategy Particle Swarm Optimization (MSHPSO), “Human Thinking Particle Swarm Optimization (HTPSO) and “Human Disease Particle Swarm Optimization (HDPSO)” are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.