Engineering optimization needs easy-to-use and efficient optimization tools that can be employed for practical purposes. In this context, stochastic search techniques have good reputation and wide acceptability as being powerful tools for solving complex engineering optimization problems. However, increased complexity of some metaheuristic algorithms sometimes makes it difficult for engineers t...

Most Data Mining tasks are performed by the application of Machine Learning techniques. Metaheuristic approaches are becoming very useful for designing efficient tools in Machine Learning. Metaheuristics are general strategies to design efficient heuristic procedures. Scatter Search is a recent metaheuristic that has been successfully applied to solve standard problems in three central paradigm...

One significant task addressed during software development project is to determine which features should be covered by the application that is being developed. This problem is known as the Next Release Problem (NRP) and has been solved using metaheuristic search techniques. We show how to apply these techniques by its embedding into a requirement management tool as an assistant functionality. W...

Annual Crop Planning (ACP) is an NP-hard-type optimization problem in agricultural planning. It involves finding optimal solutions concerning the seasonal allocations of a limited amount of agricultural land amongst the various competing crops that are required to be grown on it. This study investigates the effectiveness of employing three new local search (LS) metaheuristic techniques in deter...

This research is focused on solving problems in the area of software project management using metaheuristic search algorithmsand as such is research in the field of search based software engineering. The main aim of this research is to evaluate the performance of different metaheuristic search techniques in resource allocation and scheduling problemsthat would be typical of software development...

The Graph Partitioning Problem (GPP) is one of the most studied NPcomplete problems notable for its broad spectrum of applicability such as in VLSI design, data mining, image segmentation, etc. Due to its high computational complexity, a large number of approximate approaches have been reported in the literature. Hybrid algorithms that are based on adaptations of popular metaheuristic technique...

Most real-world search and optimization problems involve complexities such as non-convexity, nonlinearities, discontinuities, mixed nature of variables, multiple disciplines and large dimensionality, a combination of which renders classical provable algorithms to be either ineffective, impractical or inapplicable. There do not exist any known mathematically motivated algorithms for finding the ...

A new metaheuristic method applied to the global path planning for mobile robots in dynamic environments is presented. This algorithm, named the Quad Harmony Search method, consists of dividing the robot‟s environment into free regions by applying the Quad-tree algorithm and utilizing this information to accelerate the next phase which implements the Harmony Search optimization method to provid...

We present a new test data generation technique which uses the concept of diversity of test sets as a basis for the diversity oriented test data generation – DOTG. Using DOTG we translate into an automatic test data generation technique the intuitive belief that increasing the variety, or diversity, of the test data used to test a program can lead to an improvement on the completeness, or quali...