This study presents a methodology for quantifying the tradeoffs between sampling costs and local concentration estimation errors in an existing groundwater monitoring network. The method utilizes historical data at a single snapshot in time to identify potential spatial redundancies within a monitoring network. Spatially redundant points are defined to be monitoring locations that do not apprec...

The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. There are security domains where the payoffs for preventing the different types of adversaries may take different forms (seized money, reduced crime, saved lives, etc) which are not readily comparable. Thus, it can be difficu...

We develop a rule-based optimization approach. The rules are IF-THEN statements that allow domain experts to model their complex systems in a natural framework. We formalize the enterprise problem as minimizing multi-criteria objectives with respect to a set of business rules. We construct a finite state machine in which the rules correspond to state transitions. The states are evaluated by an ...

This work presents a new evolutionary approach to searching for a global solution (in the Pareto sense) to multiobjective optimisation problem. Novelty of the method proposed consists in the application of an evolutionary multi-agent system (EMAS) instead of classical evolutionary algorithms. Decentralisation of the evolution process in EMAS allows for intensive exploration of the search space,...

This paper presents a Pareto-based bi-objective optimization of hazardous materials vehicle routing and scheduling problem with time windows and shows its application to a realistic hazardous material logistics instance. A meta-heuristic solution algorithm is also proposed, which returns a set of routing solutions that approximate the frontier of the Pareto optimal solutions based on total sche...

This work introduces a new evolutionary approach to search for a global solution to multiobjective optimisation problem in the Pareto sense. Novelty of the proposed method consists in application of an evolutionary multi-agent system (EMAS) instead of classical evolutionary algorithms. Decentralisation of the evolution process in EMAS allows for intensive exploration of the search space and eff...

In this contribution we analyze the effect that mutual information has on the actual performance of efficient negotiation methods. Specifically, we start by proposing the theoretical notion of Abstract Negotiation Method (ANM) as a map from the negotiation domain in itself, for any utility profile of the parties. ANM can face both direct and iterative negotiations, since we show that ANM class ...

As current electric power communication network planning can hardly consider multiple design objectives simultaneously, we proposed a general optimization model of multiple objectives optical network planning in electric power communication systems, based on Pareto optimization and genetic algorithms. The optical network in power system is modelled mathematically and the total cost functional i...

In this paper, we present the development and application of a Technical Feasibility Model (TFM) used in preliminary design to determine: whether or not a set of desired product specifications is technically feasible, and the optimality of those specifications with respect to the Pareto frontier. The TFM is developed by integrating the capabilities of a multidisciplinary design framework, a mul...

Obtaining the fullest possible representation of solutions to a multi-objective optimization problem has been a major concern in MultiObjective Genetic Algorithms (MOGAs). This is because a MOGA, due to its nature, usually produces several clusters of solutions that does not cover the whole range of Pareto frontier. This poster paper indroduces an overview of a new approach, one that aims at ob...