The planning phase of every construction project is entangled with multiple and occasionally conflicting criteria which need to be optimized simultaneously. Multi-criterion decision-making (MCDM) approaches can aid decision-makers in selecting the most appropriate solution among numerous potential Pareto optimal solutions. An evidential reasoning (ER) approach was applied for the first time in ...

Pareto optimization solves a constrained optimization task by reformulating the task as a bi-objective problem. Pareto optimization has been shown quite effective in applications; however, it has little theoretical support. This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimiza...

The conventional weighted aggregation method is extended to realize multi-objective optimization. The basic idea is that systematically changing the weights during evolution will lead the population to the Pareto front. Two possible methods are investigated. One method is to assign a uniformly distributed random weight to each individual in the population in each generation. The other method is...

In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns a...

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto–optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. ...

In this paper a new MultiOBjective Evolution Strategy (MOBES) for solving multi-objective optimization problems subject to linear and nonlinear constraints is presented. MOBES is based on the new concept of C-, F-and N-tness, which allows systematically to handle constraints and (in)feasible individuals. The existence of niche infeasible individuals in every population enables to explore new ar...

A method for incorporating fuzzy preferences into evolutionary multiobjective optimization is proposed. After introducing three commonly used models for describing fuzzy preferences, a method to convert fuzzy preferences into realvalued weight intervals is suggested. It is argued that to convert fuzzy preferences into interval-based weights is more consistent with the motivation of using fuzzy ...

In this paper, a fuzzy multi-objective constrained optimization problem (FMCOP) is converted to a single-objective constrained optimization problem with equality and inequality constraints (SCOP) by using α−level set of the fuzzy vector and weighting approach. A trust-region algorithm for solving problem (SCOP) is introduced to obtain α−pareto optimal solutions of problem (FMCOP). An εα−stabili...

Recently, a couple of approaches have been developed that combine multiobjective optimization with direct discretization methods to approximate trajectories of optimal control problems, resulting in restricted optimization problems of high dimension. The solution set of a multiobjective optimization problem is called the Pareto set which consists of optimal compromise solutions. A common way to...

In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space – also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different cha...