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.

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Evolutionary algorithms for solving multi-objective problems

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

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

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

Multi-Criteria Decision Making (MCDM) techniques are gaining popularity in sustainable energy management. The techniques provide solutions to the problems involving conflicting and multiple objectives. Several methods based on weighted averages, priority setting, outranking, fuzzy principles and their combinations are employed for energy planning decisions. A review of more than 90 published papers is presented here to analyze the applicability of various methods discussed. A classification on application areas and the year of application is presented to highlight the trends. It is observed that Analytical Hierarchy Process is the most popular technique followed by outranking techniques PROMETHEE and ELECTRE. Validation of results with multiple methods, development of interactive decision support systems and application of fuzzy methods to tackle uncertainties in the data is observed in the published literature.

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Application of multi-criteria decision making to sustainable energy planning—A review

Foreword. Preface. About the Authors. 1. Decision-Aiding Today: What Should We Expect B. Roy. 2. Theory of Vectormaximization: Various Concepts of Efficient Solutions J. Jahn. 3. Duality in Multi-objective Optimization H. Nakayama. 4. Preference Relations and MCDM C.A. Bana e Costa, J.-C. Vansnick. 5. Normative and Descriptive Aspects of Decision Making O.I. Larichev. 6. Meta Decision Problems in Multiple Criteria Decision Making T. Hanne. 7. Sensitivity Analysis in MCDM T. Tanino. 8. Goal Programming S.M. Lee, D.L. Olson. 9. Reference Point Approaches A.P. Wierzbicki. 10. Concepts of Interactive Programming T.J. Stewart. 11. Outranking Approach P. Vincke. 12. Multi-Criteria Problem Structuring and Analysis in a Value Theory Framework V. Belton. 13. Fundamentals of Interior Multiple Objective Linear Programming Algorithms A. Arbel. 14. The Use of Rough Sets and Fuzzy Sets in MCDM S. Greco, et al. 15. Use of Artificial Intelligence in MCDM P. Perny, J.-C. Pomerol. 16. Evolutionary Algorithms and Simulated Annealing for MCDM A.J. Chipperfield, et al. Index.

Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications

On the convergence of multiobjective evolutionary algorithms

Intrahospital transports are often required for diagnostic or therapeutic reasons. Depending on the hospital layout, transportation between nursing wards and service units is either provided by ambulances or by trained personnel who accompany patients on foot. In many large German hospitals, the patient transport service is poorly managed and lacks work-flow coordination. This contributes to higher hospital costs (e.g., because a patient is not delivered to the operating room on time) and to patient inconvenience (e.g., because of long waiting times). We designed a computer-based planning system, Opti-TRANS©, that supports all phases of the transportation flow, including travel booking, dispatching transport requests, and monitoring and reporting trips in real time. The methodology, which we developed to solve the underlying optimization problem—a dynamic dial-a-ride problem with hospital-specific constraints, draws on fast heuristic methods to ensure the efficient and timely provision of transports. We i...

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Thomas Hanne

Papers

Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications

On the convergence of multiobjective evolutionary algorithms

Bringing robustness to patient flow management through optimized patient transports in hospitals

Multicriteria Decision Making

Bringing robustness to patient flow management through optimized patient transports in hospitals