List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

Evolutionary algorithms for solving multi-objective problems

The resilience perspective is increasingly used as an approach for understanding the dynamics of social–ecological systems. This article presents the origin of the resilience perspective and provides an overview of its development to date. With roots in one branch of ecology and the discovery of multiple basins of attraction in ecosystems in the 1960–1970s, it inspired social and environmental scientists to challenge the dominant stable equilibrium view. The resilience approach emphasizes non-linear dynamics, thresholds, uncertainty and surprise, how periods of gradual change interplay with periods of rapid change and how such dynamics interact across temporal and spatial scales. The history was dominated by empirical observations of ecosystem dynamics interpreted in mathematical models, developing into the adaptive management approach for responding to ecosystem change. Serious attempts to integrate the social dimension is currently taking place in resilience work reflected in the large numbers of sciences involved in explorative studies and new discoveries of linked social–ecological systems. Recent advances include understanding of social processes like, social learning and social memory, mental models and knowledge–system integration, visioning and scenario building, leadership, agents and actor groups, social networks, institutional and organizational inertia and change, adaptive capacity, transformability and systems of adaptive governance that allow for management of essential ecosystem services.

Resilience: the emergence of a perspective for social-ecological systems analyses

A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.

Survey of multi-objective optimization methods for engineering

Desert Ecosystems: Environment and Producers

The integration of GIS and multicriteria decision analysis has attracted significant interest over the last 15 years or so This paper surveys the GIS‐based multicriteria decision analysis (GIS‐MCDA) approaches using a literature review and classification of articles from 1990 to 2004 An electronic search indicated that over 300 articles appeared in refereed journals The paper provides taxonomy of those articles and identifies trends and developments in GIS‐MCDA

https://www.tandfonline.com/doi/pdf/10.1080/13658810600661508

GIS‐based multicriteria decision analysis: a survey of the literature

Land suitability evaluation in a raster GIS environment is conceptualized as a multiple-criteria decision-making (MCDM) problem A combination of MCDM techniques selected for implementing the methodology included value and priority assessment techniques for scaling the interval and ordinal data respectively, and compromise programming (CP) to aggregate the unidimensional evaluations The contribution of the proposed methodology to handle problems of scaling and dependence that often affect expert-based suitability analyses is discussed A case-study of habitat evaluation for the endangered Mount Graham red squirrel is presented The multiple-criteria models resulting from the CP analysis of an expert's perception of the habitat preference structure of the red squirrel are compared with data of actual habitat use The predictive power of the models is good and sensitivity analysis based on the distance-metric parameter p of CP reveals some interesting differences between alternative strategies for

A multiple criteria decision-making approach to GIS-based land suitability evaluation

From the Publisher:
This book presents in a systematic and comprehensive manner themodeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback.
The book includes a short introduction to fuzzy logic containing basic definitions of fuzzy set theory and fuzzy rule systems. It describes methods for the assessment of rule systems, systems with discrete response sets, for modeling time series, for exact physical systems, examines verification and redundancy issues, and investigates rule response functions.
Definitions and propositions, some of which have not been published elsewhere, are provided; numerous examples as well as references to more elaborate case studies are also given. Fuzzy rule-based modeling has the potential to revolutionize fields such as hydrology because it can handle uncertainty in modeling problems too complex to be approached by a stochastic analysis. There is also excellent potential for handling large-scale systems such as regionalization or highly non-linear problems such as unsaturated groundwater pollution.

http://www.gbv.de/dms/goettingen/184611571.pdf

Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems

1. Basic Properties of Multiobjective Decision Making Problems. 2. Generating Efficient Solutions. 3. Interactive Methods. 4. Computation of Equilibrium Points. 5. Linear Problems. 6. Value Functions and Utility Theory. 7. Dynamic Multiobjective Decision Problems. 8. Selected Advanced Topics. References. Subject Index.

Techniques for multiobjective decision making in systems management

A fuzzy rule-based model is constructed to derive operation rules for a multipurpose reservoir. The case study of the Tenkiller Lake in Oklahoma illustrates the methodology. Operation rules are generated on the basis of economic development criteria such as hydropower; municipal; industrial and irrigation demands; flood control and navigation; and environmental criteria such as: water quality for fish and wildlife preservation, recreational needs, and downstream flow regulation. The fuzzy rule-based model operates on an “if-then” principle, where the “if” is a vector of fuzzy explanatory variables or premises and “then” of fuzzy consequences. The reservoir storage level, estimated inflows, and demands are used as the premises and release from the reservoir is taken as the consequence. Split sampling of historical data (mean daily time series of flow, lake level, demands, and releases) is used to train and then validate the rules. Different performance indices are calculated and two figures of merit, namely, \Iengineering sustainability\N and \Iengineering risk\N are developed for evaluating the rules generated by the model, which appears to be easy to construct, apply, and extend to a complex system of reservoirs.

Fuzzy Rule-Based Modeling of Reservoir Operation

A general methodology for fuzzy regression is developed and illustrated by an actual hydrological case study. Fuzzy regression may be used whenever a relationship between variables is imprecise and/or data are inaccurate and/or sample sizes are insufficient. In such cases fuzzy regression may be used as a complement or an alternative to statistical regression analysis. In fuzzy regression, several “goodness of fit” criteria may be used such as the maximum average vagueness criterion and the prediction vagueness criterion. The technique is illustrated by means of a case study involving the relationship between soil electrical resistivity and hydraulic permeability. This relationship is imprecise and based on only a few data points. In the present case a curvilinear relationship is fitted using fuzzy regression with six calculated resistivities and six measured permeabilities. Prediction vagueness criteria appears to yield a more robust fuzzy regression than the maximum average vagueness criteria. Potential application areas of fuzzy regression in hydrology are discussed further. The methodology is relatively simple, and the results can be interpreted to provide a valuable hydrological decision-making aid.

Lucien Duckstein

Papers

A multiple criteria decision-making approach to GIS-based land suitability evaluation

Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems

Techniques for multiobjective decision making in systems management

Fuzzy Rule-Based Modeling of Reservoir Operation

Fuzzy regression in hydrology