Happiness and Health Particle Swarm Optimization

EasyChair Preprint 15656

Abstract

Particle Swarm Optimization (PSO) is a popular and widely used optimization algorithm for solving complex problems. It is known for its simplicity and ease of implementation. Artificial Birds move in search space to find optimal solution. Although many PSO algorithms were proposed in literature the concepts like happiness and health are not yet explored in PSO algorithms. This article is based on this research gap. Happiness and Health Particle Swarm Optimization (HaHePSO) algorithm is created by incorporating the Happiness and Health concepts into Particle Swarm Optimization algorithm. Each particle in HaHePSO algorithm is associated with happiness and health variables. The movement of Artificial Birds in PSO algorithm is based on fitness values. In HaHePSO algorithm the movement of Artifical Birds is dependent on happiness, health and fitness values. In PSO algorithm Artificial Birds move in the direction of local best and global best of fitness values. This idea is extended in HaHePSO algorithm where Artificial Birds move in the direction of local best and global best of happiness, health and fitness values. The HaHePSO algorithm proposed in this article takes more space and requires extra computation compared to PSO algorithm. This is due to the fact that each particle now has happiness and health variables associated with it and movement in search space is guided by the fitness, happiness and health values.

Keyphrases: AI, Artificial Intelligence, HaHePSO, Happiness, Happiness and Health Particle Swarm Optimization, Health, PSO, Particle Swarm Optimization

@booklet{EasyChair:15656,
  author    = {Satish Gajawada},
  title     = {Happiness and Health Particle Swarm Optimization},
  howpublished = {EasyChair Preprint 15656},
  year      = {EasyChair, 2025}}