Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …

Multi-objective optimization problems having more than three objectives are referred to as
many-objective optimization problems. Many-objective optimization brings with it a number …

The test case generation is intrinsically a multi-objective problem, since the goal is covering
multiple test targets (eg, branches). Existing search-based approaches either consider one …

Fog computing aims to provide resources to cloud data centers at the network's edge to
support time-critical Internet of Things (IoT) applications with low-latency requirements …

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and
efficiency in solving problems with two or three objectives. However, recent studies show …

Abstract “Framework” and “case-study” are the two most prominent features in the
optimization of architectural building design. The first can improve the speed of the process …

In the last decade, several evolutionary algorithms have been proposed in the literature for
solving multi-and many-objective optimization problems. The performance of such …

A fast non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III)
is a well-known many-objective optimization algorithm in which the reference-point strategy …

Test data generation has been extensively investigated as a search problem, where the
search goal is to maximize the number of covered program elements (eg, branches) …

Feature selection is an important task in machine learning that has two main objectives: 1)
reducing dimensionality and 2) improving learning performance. Feature selection can be …