This study demonstrates how Arrow's Impossibility Theorem, a theory of social choice, is of direct concern when formulating water-resources systems planning problems. Traditional strategies for solving multiobject...
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This study demonstrates how Arrow's Impossibility Theorem, a theory of social choice, is of direct concern when formulating water-resources systems planning problems. Traditional strategies for solving multiobjective water resources problems typically aggregate multiple performance measures into single composite objectives (e.g., a priori preference weighting or grouping-like measures by category). Arrow's Impossibility Theorem, commonly referred to as Arrow's Paradox, implies that a subset of performance concerns will inadvertently dictate the properties of the optimized design alternative in unpredictable ways when using aggregated objectives. This study shows how many-objective planning can aid in battling Arrow's Paradox. Many-objective planning explicitly disaggregates measures of performance while supporting the discovery of planning tradeoffs, using tools such as multiobjective evolutionary algorithms (MOEAs). An urban water portfolio planning case study for the Lower Rio Grande Valley, Texas is used to demonstrate how aggregate, lower objective-count formulations can adversely bias risk-based decision support. Additionally, this study employs a comprehensive diagnostic assessment of the Borg MOEA's ability to address Arrow's Paradox by enabling users to explore problem formulations with increasing numbers of objectives and decisions. Counter to conventional assumptions, the diagnostic analysis carefully documents that for modern self-adaptive MOEA searches, increasing objective counts can lead to more effective, efficient, reliable, and controllable searches. The increased objective counts are also shown to directly reduce decision biases that can emerge from problem formulation aggregation and simplification, related to Arrow's Paradox. (C) 2015 American Society of Civil Engineers.
Abstract The emerging paradigm of grid computing provides a powerful platform for the solution of complex and computationally expensive problems. An example of this is the multi-objective evolutionary design of robust...
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Abstract The emerging paradigm of grid computing provides a powerful platform for the solution of complex and computationally expensive problems. An example of this is the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems, before using the framework in the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H ∞ loop shaping.
This paper presents a path planner for Unmanned Air Vehicles (UAVs) based on evolutionaryalgorithms (EA) that can be used in realistic risky scenarios. The path returned by the algorithm fulfills and optimizes multip...
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ISBN:
(纸本)9781605581309
This paper presents a path planner for Unmanned Air Vehicles (UAVs) based on evolutionaryalgorithms (EA) that can be used in realistic risky scenarios. The path returned by the algorithm fulfills and optimizes multiple criteria which (1) are calculated based on properties of real UAVs, terrains, radars and missiles, and (2) are used to rank the solutions according to the priority levels and goals selected for each mission. Developed originally to work with only one UAV, the planner currently allows us to obtain the optimal path of several UAVs that are flying simultaneously. It works globally offline and locally online to recalculate a part of the path when an unexpected threat appears. Finally, the effectiveness of the solutions given by this planner has been successfully tested against a simulator that implements a complex model of the UAV and its environment.
In this paper,we study the fuzzification of the pareto dominance relation and its application in Nutrition Meal Prescription.A generic ranking scheme is presented that assigns dominance degrees to any set of vectors i...
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In this paper,we study the fuzzification of the pareto dominance relation and its application in Nutrition Meal Prescription.A generic ranking scheme is presented that assigns dominance degrees to any set of vectors in a scale-independent,nonsymmetric and set-dependent *** fuzzy-based definitions of optimality and dominated solution are *** corresponding extension of the Standard Genetic Algorithm,so-called Fuzzy-Dominance-Driven MOGA（FMOGA）,will be presented as *** verify the usefulness of such an approach,the approach is tested on analytical test cases in order to show its *** solutions,provided by the proposed algorithm for the Nutrition Prescription Model,are promising when compared with an existing well-known algorithm.
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