USING PYTHON WITH NUMPY AND MATPLOT

PSO

Human Performance Index Optimization using Particle Swarm Optimization

Objective: Maximize the Human Performance Index (HPI) by optimizing a set of factors that influence human performance in a given environment.

Variables: Training Hours (TH) : The number of hours allocated for training activities. Work-Life Balance (WLB) : A measure representing the balance between work and personal life. Job Satisfaction (JS) : A subjective measure of satisfaction with one's job. Health and Wellness (HW) : A composite measure of physical and mental well-being. Communication Effectiveness (CE) : The efficiency of communication within the organization.

Constraints: Training Hours Constraint : 10≤TH≤40 hours per week. Work-Life Balance Constraint : 0≤WLB≤100, where 0 represents poor balance and 100 represents perfect balance. Job Satisfaction Constraint : 0≤JS≤100, where 0 represents extreme dissatisfaction and 100 represents extreme satisfaction. Health and Wellness Constraint : 0≤HW≤100, where 0 represents poor health and 100 represents excellent health. Communication Effectiveness Constraint : 0≤CE≤100, where 0 represents poor communication and 100 represents excellent communication.

HPI Calculation: HPI = w1 . TH + w2 . WLB + w3 . JS + w4 . HW + w5 . CE

Where: w1, w2, w3, w4, w5 are weights representing the importance of each factor The objective is to maximize HPI by adjusting the values of TH,WLB,JS,HW, and CE.

PSO Optimization: Apply Particle Swarm Optimization to find the optimal values of TH,WLB,JS,HW, and CE that maximize the Human Performance Index.