Adaptive Fuzzy Campus Placement Based Optimization Algorithm

A novel Adaptive Fuzzy Campus Placement Optimization Algorithm (AFCPOA) is developed for solving unconstrained optimization problems. The proposed optimization algorithm is based on the concept of campus placement procedure adopted for offering a job to a student by an employer visiting campus for hiring students seeking employment. Fuzzy models are considered to depict written test and interview process. The performance of the proposed algorithm was tested on 10 benchmark optimization test functions and compared with other existing algorithms. Subsequently, the proposed algorithm is applied on IEEE 33 bus radial distribution system for optimal placement and sizing of Distributed Generators (DGs) to mitigate active power losses and voltage deviation. It is observed from the results that the proposed algorithm is more effective in comparison with existing algorithms.

I. Introduction

Optimization is a process of determining the solution for which the objective function has the best value. One obvious procedure is to sample the space and perform trial and error search [1]. Traditional optimization methods such as sequential search methods are slow and gradient-based methods fail when the objective function is non-differentiable or dis-continuous. Meta-heuristic techniques bypass such issues and promise to solve almost any optimization problem. Broadly, Meta-heuristic techniques are classified into 2 types - Single-based meta-heuristics and Population-based Meta-heuristics. Population based Meta-heuristics are classified into 4 types - evolutionary, physics-based, swarm intelligence-based and human-based [2].