Journal Article•
Multiobjective Optimization
TL;DR: In this paper, a wireless communication system consists of various passive components, including antennas, directional couplers, phase shifters, and filters, and the mathematical approach may use some assumptions in the calculation that introduces differences between the calculated dimensions and the actual dimensions.
Abstract: A wireless communication system consists of various passive components, including antennas, directional couplers, phase shifters, and filters. These components have many dimensional parameters that must be determined. For instance, a coupled line section has line width, gap between the coupled lines, and the length of the line as the parameters to be determined. Identifying these parameters through trial and error is ineffective. Meanwhile, the mathematical approach may be very complex and may use some assumptions in the calculation that introduces differences between the calculated dimensions and the actual dimensions.
Citations
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01 Mar 2010
TL;DR: It is found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.
Abstract: The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.
406 citations
TL;DR: In this paper, an adaptive control algorithm is proposed to balance the need for power quality (voltage regulation) with the desire to minimize power loss in a radial distribution circuit with a high penetration of photovoltaic cells.
Abstract: We show how an adaptive control algorithm can improve the performance of distributed reactive power control in a radial distribution circuit with a high penetration of photovoltaic (PV) cells. The adaptive algorithm is designed to balance the need for power quality (voltage regulation) with the desire to minimize power loss. The adaptation law determines whether the objective function minimizes power losses or voltage regulation based on whether the voltage at each node remains close enough to the voltage at the substation. The reactive power is controlled through the inverter on the PV cells. The control signals are determined based on local instantaneous measurements of the real and reactive power at each node. We use the example of a single branch radial distribution circuit to demonstrate the ability of the adaptive scheme to effectively reduce voltage variations while simultaneously minimizing the power loss in the studied cases. Simulations verify that the adaptive schemes compares favorably with local and global schemes previously reported in the literature.
390 citations
TL;DR: In this paper, the authors present a detailed review of mathematical programming models developed for BSC and identify key challenges and potential future work, as well as detailed analysis of the BSC modeling and design.
Abstract: Biofuels are identified as the potential solution for depleting fossil fuel reserves, increasing oil prices, and providing a clean, renewable energy source The major barrier preventing the commercialization of lignocellulosic biorefineries is the complex conversion process and their respective supply chain Efficient supply chain management of a lignocellulosic biomass is crucial for success of second generation biofuels This paper systematically describes energy needs, energy targets, biofuel feedstocks, conversion processes, and finally provides a comprehensive review of Biomass Supply Chain (BSC) design and modeling Specifically, the paper presents a detailed review of mathematical programming models developed for BSC and identifies key challenges and potential future work This review will provide readers with a starting point for understanding biomass feedstocks and biofuel production as well as detailed analysis of the BSC modeling and design
334 citations
TL;DR: In this article, a multi-objective optimization model is developed to analyze the optimal operating strategy of a DER system while combining the minimization of energy cost with the minimisation of environmental impact which is assessed in terms of CO2 emissions.
329 citations
TL;DR: In this article, a solar-powered high temperature differential Stirling engine was considered for optimization using multiple criteria, including the output power and overall thermal efficiency, and the Pareto optimal frontier was obtained and a final optimal solution was also selected using various decision-making methods.
227 citations
References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.
37,111 citations
"Multiobjective Optimization" refers methods in this paper
...Crowding Distance Assignment [1], [4] Genetic Cycle...
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...NS genetic algorithm II (NSGA-II) [1], [4] is one of the formulations of an MOEA....
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...The fast NS process [1], [4] compares a set of solutions and divides it into...
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Book•
30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Abstract: 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.
5,994 citations
4,215 citations
"Multiobjective Optimization" refers methods in this paper
...Powell’s method [9] is a conjugate gradient descent method for finding a local minimum of a function of several variables without calculating derivatives....
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Book•
27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
Abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.
2,062 citations