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

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

: This research organizes, presents, and analyzes contemporary Multiobjective Evolutionary Algorithm (MOEA) research and associated Multiobjective Optimization Problems (MOPs). Using a consistent MOEA terminology and notation, each cited MOEAs' key factors are presented in tabular form for ease of MOEA identification and selection. A detailed quantitative and qualitative MOEA analysis is presented, providing a basis for conclusions about various MOEA-related issues. The traditional notion of building blocks is extended to the MOP domain in an effort to develop more effective and efficient MOEAs. Additionally, the MOEA community's limited test suites contain various functions whose origins and rationale for use are often unknown. Thus, using general test suite guidelines appropriate MOEA test function suites are substantiated and generated. An experimental methodology incorporating a solution database and appropriate metrics is offered as a proposed evaluation framework allowing absolute comparisons of specific MOEA approaches. Taken together, this document's classifications, analyses, and new innovations present a complete, contemporary view of current MOEA "state of the art" and possible future research. Researchers with basic EA knowledge may also use part of it as a largely self-contained introduction to MOEAs.

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

Evolutionary algorithms in control systems engineering: a survey

This paper reviews the particle emissions formed during the combustion process in spark ignition and diesel engine. Proposed legislation in Europe and California will impose a particle number requirement for GDI (gasoline direct injection) vehicles and will introduce the Euro 6 and LEV-III emission standards. More careful optimization for reducing particulate emission on engine hardware, fuel system, and control strategy to reduce particulate emissions will be required during cold start and warm-up phases. Because The diesel combustion inherently produces significant amounts of PM as a result of incomplete combustion around individual fuel droplets in the combustion zone, much attention has been paid to reducing particle emissions through electronic engine control, high pressure injection systems, combustion chamber design, and exhaust after-treatment technologies. In this paper, recent research and development trends to reduce the particle emissions from internal combustion engines are summarized, with a focus on PMP activity in EU, CARB and SAE papers and including both state-of-the-art light-duty vehicles and heavy-duty engines.

Exhaust nanoparticle emissions from internal combustion engines: A review

Ultrafast X-ray study of multi-hole GDI injector sprays: Effects of nozzle hole length and number on initial spray formation

Combustion Technology Development for a High Compression Ratio SI Engine

A genetic algorithm based optimization method is proposed for a multi-objective design problem of an automotive engine, that includes several difficulties in practical engineerin g optimization problems. While various optimization techniques have been applied to engineering design problems, a class of realistic engineering design problems face on a mixture of different optimization difficulties, such as the rugged nat ure of system response, the numbers of design variables and objectives, etc. In order to overcome such a situation, this paper proposes a genetic algorithm based multi-objective optimization method, that introduces Pareto-optimality based fitness fu nction, similarity based selection and direct real number crossove r. This optimization method is also applied to the design problem of an automotive engine with the design criteria on a total power t rain. The computational examples show the ability of the proposed method for finding a relevant set of Pareto optima.

Hideaki Yokohata

Papers

Ultrafast X-ray study of multi-hole GDI injector sprays: Effects of nozzle hole length and number on initial spray formation

Combustion Technology Development for a High Compression Ratio SI Engine

Multi-Objective Optimal Design of Automotive Engine Using Genetic Algorithm

A comprehensive study on the factors affecting near-nozzle spray dynamics of multi-hole GDI injectors

Governing parameters and dynamics of turbulent spray atomization from modern GDI injectors