Most multi-objective evolutionary algorithms (MOEAs) use the concept of dominance in the
search process to select the top solutions as parents in an elitist manner. However, as …

Among numerous multi-objective optimization algorithms, the Elitist non-dominated sorting
genetic algorithm (NSGA-II) is one of the most popular methods due to its simplicity …

Large construction and infrastructure projects are a billion-dollar business, but few studies
have addressed the integrated operations in this unique domain of the construction supply …

A multi-objective memetic algorithm based on decomposition is proposed in this article, in
which a simplified quadratic approximation (SQA) is employed as a local search operator for …

In this paper a new hybridised version of non-dominated sorting genetic algorithm (NSGA) is
proposed to solve combinatorial optimisation problems associated with laminated composite …

The non-dominate sorting genetic algorithmic-II (NSGA-II) is an effective algorithm for finding
Pareto-optimal front for multi-objective optimization problems. To further enhance the …

Many of the real-world issues have multiple conflicting objectives that the optimization
between contradictory objectives is very difficult. In recent years, the Multi-objective …

Multi-objective optimization (MO) is a highly demanding research topic because many
realworld optimization problems consist of contradictory criteria or objectives. Considering …

At run time, software systems often face a myriad of adverse environmental conditions and
system failures that cannot be anticipated during the system's initial design phase. These …

Optimization of spinning conditions plays a key role in the development of high performance
asymmetric hollow fiber membranes. However, from previous studies, in solving these …