An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences

Many-objective optimization is an area of interest common to researchers, professionals, and practitioners because of its real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) is one of the current approaches to treat Many-Objective Optimization Problems (MaOPs). Some recent studies have focused on the advantages of embedding preference models based on interval outranking into MOEAs; several models have been proposed to achieve it. Since there are many factors influencing the choice of the best outranking model, there is no clear notion of which is the best model to incorporate the preferences of the decision maker into a particular problem. This paper proposes a hyper-heuristic algorithm—named HyperACO—that searches for the best combination of several interval outranking models embedded into MOEAs to solve MaOPs. HyperACO is able not only to select the most appropriate model but also to combine the already existing models to solve a specific MaOP correctly. The results obtained on the DTLZ and WFG test suites corroborate that HyperACO can hybridize MOEAs with a combined preference model that is suitable to the problem being solved. Performance comparisons with other state-of-the-art MOEAs and tests for statistical significance validate this conclusion

Methodological review of multicriteria optimization techniques: aplications in water resources

Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use

Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP into multiple single-optimization sub-problems, achieving better diversity and a better approximation of the Pareto front, and dealing with some of the challenges of MaOPs. However, these approaches still require one to solve a multi-criteria selection problem that will allow a Decision-Maker (DM) to choose the final solution. Incorporating preferences may provide results that are closer to the region of interest of a DM. Most of the proposals to integrate preferences in decomposition-based MOEAs prefer progressive articulation over the “a priori” incorporation of preferences. Progressive articulation methods can hardly work without comparable and transitive preferences, and they can significantly increase the cognitive effort required of a DM. On the other hand, the “a priori” strategies do not demand transitive judgements from the DM but require a direct parameter elicitation that usually is subject to imprecision. Outranking approaches have properties that allow them to suitably handle non-transitive preferences, veto conditions, and incomparability, which are typical characteristics of many real DMs. This paper explores how to incorporate DM preferences into MOEA/D using the “a priori” incorporation of preferences, based on interval outranking relations, to handle imprecision when preference parameters are elicited. Several experiments make it possible to analyze the proposal's performance on benchmark problems and to compare the results with the classic MOEA/D without preference incorporation and with a recent, state-of-the-art preference-based decomposition algorithm. In many instances, our results are closer to the Region of Interest, particularly when the number of objectives increases

Multi-criteria decision-making in whole process design

PhD ThesisIn recent years, the chemical and pharmaceutical industries have faced increased development times and costs with fewer novel chemicals being discovered. This has resulted in many companies focusing on innovative research and development as they consider this key to business success. In particular, a number of leading industrial organisations have adopted the principles of Whole Process Design (WPD). WPD considers the optimisation of the entire product development process, from raw materials to end product, rather than focusing on each individual unit operation. The complexity involved in the implementation of WPD requires rationalised decision-making, often with limited or uncertain information. This thesis assesses the most widely applied methods in Multi-Criteria Decision Analysis (MCDA) in conjunction with the results of two interviews and two questionnaires that identified the industrial requirements for decision-making during WPD. From the findings of this work, a novel decision-making methodology was proposed, the outcome of which allows a decision-maker to visually interpret their decision results with associated levels of uncertainty. To validate the proposed methodology, a software framework was developed that incorporates two other decision-making approaches, the Analytical Hierarchy Process (AHP) and ELimination Et Choix Traduisant la REalité trois (ELECTRE III). The framework was then applied to a number of industrial case studies to validate the application of the proposed methodology.Engineering and Physical Sciences Research Council (EPSRC) and Chemistry Innovatio

Assessment of Multi-Criteria Preference Measurement Methods for a Dynamic Environment

Multi-criteria decision analysis is required in various domains where decision making reoccurs as part of a longer-term process. When the decision context changes or the preferences evolve due to process dynamics, one-shot preference measurement is not sufficient to build an adequate basis for decision making. Process dynamics require taking into account the dimension of time. We investigate six interactive preference measurement methods providing the possibility to assess alternatives in terms of utility for an individual decision maker, whether they are suitable for dynamic preference adjustment. We use a mixed-methods approach to analyse them towards 1) requirements for a dynamic method, and 2) their efficiency, validity, and complexity. Our results show that the best method to be further developed for dynamic context is Adaptive Self-Explication slightly preferable over Pre-Sorted Self-Explication. Our assessment implicates that an extension of the Adaptive Self-Explication will enable efficient dynamic decision support

Inferring parameters of a relational system of preferences from assignment examples using an evolutionary algorithm

Most evolutionary multi-objective algorithms perform poorly in many objective problems. They normally do not make selective pressure towards the Region of Interest (RoI), the privileged zone in the Pareto frontier that contains solutions important to a DM.  Several works have proved that a priori incorporation of preferences improves convergence towards the RoI. The work of (E. Fernandez, E. Lopez, F. Lopez & C.A. Coello Coello, 2011) uses a binary fuzzy outranking relational system to map many-objective problems into a tri-objective optimization problem that searches the RoI; however, it requires the elicitation of many preference parameters, a very hard task. The use of an indirect elicitation approach overcomes such situation by allowing the parameter inference from a battery of examples.  Even though the relational system of Fernandez et al. (2011) is based on binary relations, it is more convenient to elicit its parameters from assignment examples. In this sense, this paper proposes an evolutionary-based indirect parameter elicitation method that uses preference information embedded in assignment examples, and it offers an analysis of their impact in a priori incorporation of DM’s preferences. Results show, through an extensive computer experiment over random test sets, that the method estimates properly the model parameter’s values. First published online 7 May 201

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications