As the name suggests, multi-objective optimisation involves optimising a number of
objectives simultaneously. The problem becomes challenging when the objectives are of …

Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …

Using surrogate models in evolutionary search provides an efficient means of handling
today's complex applications plagued with increasing high-computational needs. Recent …

This paper provides a survey of the research done on optimization in dynamic environments
over the past decade. We show an analysis of the most commonly used problems, methods …

GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-
objective optimization that is designed to obtain good non-dominated solutions to black box …

Today, bridge design seeks not only to minimize cost, but also to minimize adverse
environmental and social impacts. This multi-criteria decision-making problem is subject to …

The paper shows how cost-reduction methods can be synergistically combined to enable
high-fidelity hull-form optimization under stochastic conditions. Specifically, a multi-objective …

In order to minimize the total expected cost, bridges have to be designed for safety and
durability. This paper considers the cost, the safety, and the corrosion initiation time to …

Evolutionary algorithms (EA s) have amply shown their promise in solving various search
and optimization problems for the past three decades. One of the hallmarks and niches of …

Building system design optimization is becoming popular for design decision making. State-
of-the-art technique that couples evolutionary algorithms with a building simulation engine …