To approximate the Pareto front, most existing multiobjective evolutionary algorithms store
the nondominated solutions found so far in the population or in an external archive during …

During the optimization process, a large number of data are generated through the search.
Machine learning techniques and algorithms can be used to handle the generated data to …

In the last decade, retrofitting strategies have been reviewed to improve energy efficiency
and reduce the environmental impact of existing buildings. One retrofitting strategy consists …

The set of available multi-objective optimisation algorithms continues to grow. This fact can
be partially attributed to their widespread use and applicability. However, this increase also …

Abstract Many-Objective Optimization Problems, or MaOPs, are complex optimization
problems with more than three objective functions. Traditional Multi-Objective Evolutionary …

In multiobjective decision making, most knee identification algorithms implicitly assume that
the given solutions are well distributed and can provide sufficient information for identifying …

Multi-objective optimization problem resolution using Evolutionary Algorithms (EAs) has not
yet been completely addressed. Issues such as the population diversity loss and the EA …

Large optimization problems that involve either a large number of decision variables or
many objectives pose great challenges to nature inspired optimization algorithms. On the …

This work introduces the following concepts of directional and estimated directional Pareto
front to encourage multi-objective decision making, especially when the Pareto front exists in …

For continuous multi-objective optimization prob-lems there exists an infinite number of
solutions on the Paretooptimal front. A multi-objective evolutionary algorithm attempts to find …