Novel Artificial Human Optimization Field Algorithms - The Beginning

New Artificial Human 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 Human 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 Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human 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 Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human 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.Comment: 25 pages, 41 figure

Particle Swarm Optimization for Solving Nonlinear Programming Problems

In the beginning we provide a brief introduction to the basic concepts of optimization and global optimization, evolutionary computation and swarm intelligence. The necessity of solving optimization problems is outlined and various types of optimization problems are discussed. A rough classfication of established optimization algorithms is provided, followed by Particle Swarm Optimization (PSO) and different types of PSO. Change in velocity component using velocity clamping techniques by bisection method and golden search method are discussed. We have discussed advantages of Using Self-Accelerated Smart Particle Swarm Optimization (SAS-PSO) technique which was introduced . Finally, the numerical values of the objective function are calculated which are optimal solution for the problem. The SAS-PSO and Standard Particle Swarm Optimization technique is compared as a result SAS-PSO does not require any additional parameter like acceleration coefficient and inertia-weight as in case of other standard PSO algorithms

Building nearest prototype classifiers using a Michigan approach PSO

IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results

Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1