There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Introduction to linear and nonlinear programming

: 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

The paper proposes a complete real-time control algorithm for autonomous collision-free operations of the quadrotor UAV. As opposed to fixed wing vehicles the quadrotor is a small agile vehicle which might be more suitable for the variety of specific applications including search and rescue, surveillance and remote inspection. The developed control system incorporates both trajectory planning and path following. Using a differential flatness property the trajectory planning is posed as a constrained optimization problem in the output space (as opposed to the control space), which simplifies the problem. The trajectory and speed profile are parameterized to reduce the problem to a finite dimensional problem. To optimize the speed profile independently of the trajectory a virtual argument is used as opposed to time. A path following portion of the proposed algorithm uses a standard linear multi-variable control technique. The paper presents the results of simulations to demonstrate the suitability of the proposed control algorithm.

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A prototype of an autonomous controller for a quadrotor UAV

The misalignment and displacement of inductively coupled coils in a wireless power transfer system (WPT) can degrade the power efficiency and limit the amount of power that can be transferred. Coil misalignment leads the primary coil driver to operate in an untuned state which causes nonoptimum switching operation and results in an increase in switching losses. This paper presents a novel method to electronically tune a Class-E inverter used as a primary coil driver in an inductive WPT system to minimize the detrimental effects of misalignment between the inductively coupled coils which may occur during operation. The tuning method uses current-controlled inductors (saturable reactors) and a variable switching frequency to achieve optimum switching conditions regardless of the misalignment. Mathematical analysis is performed on a Class-E inverter based on an improved model of a resonant inductive link. Experimental results are presented to confirm the analysis approach and the suitability of the proposed tuning method.

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Electronic Tuning of Misaligned Coils in Wireless Power Transfer Systems

Multi-objective Optimisation and Control

A three-term backpropagation algorithm

This paper investigates the performance of Class E inverters used in wireless power transfer applications based on resonant inductive coupling. The variations in the load and the distance between the coils cause Class E inverters to operate under nonoptimal switching conditions, which result in inefficient operation and can lead to permanent damage to its switching transistor. Therefore, a novel approach to tune Class E inverters electronically is proposed. The tuning method relies on saturable reactors to ensure that the inverter operates under optimal switching conditions regardless of variations in the load and the distance between the coils. In addition, a more accurate model of inductive links is presented in order to provide a better understanding of the major power losses in resonant inductive links. Experimental results are presented to confirm the improved accuracy of the inductive link model and the validity of the tuning method.

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James F. Whidborne

Papers

A prototype of an autonomous controller for a quadrotor UAV

Electronic Tuning of Misaligned Coils in Wireless Power Transfer Systems

Multi-objective Optimisation and Control

A three-term backpropagation algorithm

Tuning Class E Inverters Applied in Inductive Links Using Saturable Reactors