Academic literature on the topic 'METAHEURISTIC OPTIMIZATION TECHNIQUES'

Straipsnyje aptariami kombinatorinio optimizavimo ir intelektualių optimizavimo priemonių, t. y. metaeuristinių metodų (metaeuristikų), teoriniai aspektai. Apibūdinami kombinatorinio optimizavimo uždaviniai, jų savybės, specifika. Pagrindinis dėmesys skiriamas metaeuristinių optimizavimo metodų charakterizavimui būtent kombinatorinio optimizavimo kontekste. Trumpai formuluojami metaeuristinių metodų tikslai, bendrosios nuostatos, taip pat akcentuojamas šių metodų savitumas, modernumas.Išsamiau apžvelgiami skiriamieji metaeuristikų bruožai, aprašomos svarbesnės teorinės metaeuristinių metodų aiškinimo kryptys. Pabaigoje pateikiamos apibendrinamosios pastabos.Combinatorial optimization and metaheuristic methods: theoretical aspectsAlfonsas Misevičius, Vytautas Bukšnaitis, Jonas Blonskis SummaryIn this paper, theoretical aspects of combinatorial optimization (CO) and intelligent optimization techniques, i. e. metaheuristic methods (metaheuristics) are discussed. The combinatorial optimization problems and their basic properties are shortly introduced. Much of our attention is paid to the characterization of the metaheuristic methods, in particular for solving CO problems. We formulate the main goals of the metaheuristic methods, also focusing on the special theoretical issues and features of these methods. The most important interpretations of the metaheuristic methods are described in more details. The paper is completed with the concluding remarks.

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic end-effector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton’s method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.

Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is extended to present differential evolution and teaching-learning-based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain.