A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks

A climate of increasing deregulation in traditional highway transportation, where the private sector has an expanded role in the provision of traditional transportation services, provides a background for practical policy issues to be investigated. One of the key issues of interest, and the focus of this chapter, would be the equilibrium decision variables offered by participants in this market. By assuming that the private sector participants play a Nash game, the above problem can be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from the classical Cournot-Nash game because each and every player’s actions is constrained by another variational inequality describing the equilibrium route choice of users on the network. In this chapter, we discuss this BLVI and suggest a heuristic coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm is subsequently tested on example problems drawn from the literature. The numerical experiments suggest that the proposed algorithm is a viable solution method for this problem

Optimization of force-limiting seismic devices connecting structural subsystems

This paper is focused on the optimum design of an original force-limiting floor anchorage system for the seismic protection of reinforced concrete (RC) dual wall-frame buildings. This protection strategy is based on the interposition of elasto-plastic links between two structural subsystems, namely the lateral force resisting system (LFRS) and the gravity load resisting system (GLRS). The most efficient configuration accounting for the optimal position and mechanical characteristics of the nonlinear devices is obtained numerically by means of a modified constrained differential evolution algorithm. A 12-storey prototype RC dual wall-frame building is considered to demonstrate the effectiveness of the seismic protection strategy

Deployment of an agent-based SANET architecture for healthcare services

This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare

Sensor actor network modeling utilizing the holonic architectural framework

This paper discusses the results of utilizing advanced EKM modeling techniques to manage Sensor-Actor networks (SANETs) based upon the Holonic Architectural Framework. EKMs allow a quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize from a given input space to administer SANET routing and clustering functions with a control parameter space. Results demonstrate that in comparison to linear approximation techniques, indirect mapping with EKMs provide fluid control and feedback mechanisms by operating in a continuous sensory control space-thus enabling interactive detection and optimization of events in real-time environments

Cooperative agent-based SANET architecture for personalised healthcare monitoring

The application of an software agent-based computational technique that implements Extended Kohonen Maps (EKMs) for the management of Sensor-Actuator networks (SANETs) in health-care facilities. The agent-based model incorporates the BDI (Belief-Desire-Intention) Agent paradigms by Georgeff et al. EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize. Current results show a combinatorial approach to optimization with EKMs provides an improvement in event trajectory estimation compared to standalone cooperative EKM processes to allow responsive event detection for patient monitoring scenarios. This will allow healthcare professionals to focus less on administrative tasks, and more on improving patient needs, particularly with people who are in need for dedicated care and round-the-clock monitoring. ©2010 IEEE

Multi-Objective Optimal Placement of Recloser and Sectionalizer in Electricity Distribution Feeders

© 2019 IEEE. Electricity distribution feeders, due to their geographical dispersion, are subjected to faults caused by adverse weather, vegetation growth, etc., resulting in long outages for customers. Overhead switching devices (i.e. reclosers, sectionalizers, disconnectors and etc.) are known as the most practical solutions to limit the outage area, and consequently increase the distribution system reliability. This paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for Optimal Placement of Recloser and Sectionalizer to minimize customers' outage cost and increase system reliability with an optimal investment. The algorithm determines the number and optimal locations of reclosers and sectionalizers to fulfill the objectives. The obtained results on the standard 85-node distribution feeder validate the effectiveness of the proposed method