Multi-Objective Optimization of EDM Process Parameters by Using Passing Vehicle Search (PVS) Algorithm

Abstract

Electro-Discharge Machining (EDM) is very popular for machining high-strength conductive materials for aerospace and automotive application. These machining involve a range of processing parameters. In order to optimize these for the best performance, a trade-off has to be decided for the responses achieved through machining. Conventional algorithms have long been replaced by advanced optimization algorithms. Performance of meta-heuristic algorithms in relation to traditional deterministic approaches for multi-modal, non-linear engineering problems is very promising in recent days. In this paper, a multi-objective optimization approach is applied using a population-based meta-heuristic algorithm called Passing Vehicle Search (PVS) for optimizing process parameters of various mathematical models formulated by different authors. Different approaches depending on case have been adopted for formulating the multi-objective PVS algorithm and pareto front is obtained for each case to extract the desired results. The performance of multi-objective PVS is compared with different intelligent computing models employed in prior studies and better results are shown in case of former. This approach can be extended to various mathematical models besides those covered in the paper to obtain better optimization results.