A Review of Current Techniques for Optimization

Abstract

Regression testing, an inevitable and costly operation often conducted under time and resource constraints, demands efficiency in reducing testing time by minimizing necessary test cases. This article explores strategies for optimizing test cases to achieve optimal fault coverage, comparing and contrasting various approaches. The discussion encompasses a range of accessible optimization strategies, providing insights into their methodologies, applications, evolution, and performance. Recent advancements in optimization techniques, such as the Genetic Algorithm, Ant Colony Method, Honey Bee Algorithm, and Particle Swarm Intelligence Optimization, are also reviewed, offering a comprehensive overview of contemporary practices in this field. Without taking into account specific situations such as convex problems, multi-objective optimization issues, linear programming, multidisciplinary optimization problems, etc., the review maintains its generic nature. Optimization problem formulation, methods of optimization, and solution techniques are presented.