Search results for "Multi-Objective Optimization"
showing 10 items of 192 documents
Water distribution network robust design based on energy surplus index maximization
2015
The aim of this paper is to show that energy surplus indices, such as resilience index, besides providing a very good indirect measure of water distribution network reliability to be adopted during the design phase, represent also a valuable and effective indicator of the robustness of the network in alternative network scenarios, and can thus be profitably used in condition of future demands uncertainty. The methodology adopted consisted of (I) multi-objective design optimization performed in order to minimize construction costs while maximizing the resilience index; (II) retrospective performance assessment of the alternative solutions of the Pareto front obtained, under demand conditions…
Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case
2017
Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization. For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive …
Multi-objective parameter identification via ACOR algorithm
2009
The spreading of advanced constituive models, needed to model complex phenomena, makes necessary to solve difficult parameter identification problems. The need of multiple tests to fully characterize the experimental behaviour makes the parameter identification problem a multi objective one. Unlike conventional techniques, based on the formulation of an aggregate scalar ob- jective function, in the present work the problem is addressed using a new multi objective algorithm obtained extending the continuous Ant Colony Optimization algorithm. Mathematical tests and ap- plication to a real world problem are performed and different performance measures are used to asses the performance of the a…
Interactively Learning the Preferences of a Decision Maker in Multi-objective Optimization Utilizing Belief-rules
2020
Many real life problems can be modelled as multiobjective optimization problems. Such problems often consist of multiple conflicting objectives to be optimized simultaneously. Multiple optimal solutions exist to these problems, and a single solution cannot be said to be the best without preferences given by a domain expert. Preferences can be used to find satisfying solutions: optimal solutions, which best match the expert’s preferences. To model the preferences of the expert, and aid him/her in finding satisfying solutions, a novel method is proposed. The method utilizes machine learning combined with belief-rule based systems to adaptively train a belief rule based system to learn a domai…
Why Use Interactive Multi-Objective Optimization in Chemical Process Design?
2008
Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements on the optimization methods used. In this chapter, we point out some shortcomings of some widely used basic methods of multi-objective optimization. As an alternative, we suggest using interactive approaches where the role of a decision maker or a designer is emphasized. Interactive multi-objective optimization has been shown to suit well for chemical process design problems because it takes the preferences of the decision maker into account in an iterative manner tha…
Exact extension of the DIRECT algorithm to multiple objectives
2019
The direct algorithm has been recognized as an efficient global optimization method which has few requirements of regularity and has proven to be globally convergent in general cases. direct has been an inspiration or has been used as a component for many multiobjective optimization algorithms. We propose an exact and as genuine as possible extension of the direct method for multiple objectives, providing a proof of global convergence (i.e., a guarantee that in an infinite time the algorithm becomes everywhere dense). We test the efficiency of the algorithm on a nonlinear and nonconvex vector function. peerReviewed
Wastewater treatment: New insight provided by interactive multiobjective optimization
2011
In this paper, we describe a new interactive tool developed for wastewater treatment plant design. The tool is aimed at supporting the designer in designing new wastewater treatment plants as well as optimizing the performance of already available plants. The idea is to utilize interactive multiobjective optimization which enables the designer to consider the design with respect to several conflicting evaluation criteria simultaneously. This is more important than ever because the requirements for wastewater treatment plants are getting tighter and tighter from both environmental and economical reasons. By combining a process simulator to simulate wastewater treatment and an interactive mul…
E-NAUTILUS: A decision support system for complex multiobjective optimization problems based on the NAUTILUS method
2015
Interactive multiobjective optimization methods cannot necessarily be easily used when (industrial) multiobjective optimization problems are involved. There are at least two important factors to be considered with any interactive method: computationally expensive functions and aspects of human behavior. In this paper, we propose a method based on the existing NAUTILUS method and call it the Enhanced NAUTILUS (E-NAUTILUS) method. This method borrows the motivation of NAUTILUS along with the human aspects related to avoiding trading-off and anchoring bias and extends its applicability for computationally expensive multiobjective optimization problems. In the E-NAUTILUS method, a set of Pareto…
Interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy
2012
Abstract We present an approach to interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy. The approach relies on formulae for lower and upper bounds on coordinates of the outcome of an arbitrary efficient variant corresponding to preference information expressed by the Decision Maker. In contrast to earlier works on that subject, here lower and upper bounds can be calculated and their accuracy controlled entirely within evolutionary computation framework. This is made possible by exploration of not only the region of feasible variants – a standard within evolutionary optimization, but also the region of i…
Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives
2013
A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat - a perennial problem in genetic …