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

Creating Space, or Just Juggling? Exploring the Adoption of Innovation in Community Sport

Previous research into community sport organization (CSO) has focused heavily on capacity and resource deficits and the ways in which CSOs manage under these constraints. This study explores mechanisms influencing CSOs as they adopt and implement an innovation: Long-Term Athlete Development (LTAD). A critical realist, extensive-intensive design spanning 36 months was used. The first, extensive phase of the study examines the contextual mechanisms influencing the approach of CSOs to adopting the LTAD innovation. Resource dependence and institutional perspectives are integrated to describe the forces acting on CSOs, how these manifest in structures, and how the structures channel the agency of CSO leaders as they work to balance resources and deliver programs. A contextual model of CSO operation under conflicting institutional logics is presented. The second, intensive phase examines the question of how CSOs plan, learn, and consolidate learning into structure as they integrate an innovation. Here, an engaged case study methodology was used to focus on the efforts of a single CSO over a one-year period as it worked to implement LTAD while managing multiple resource constraints. A learning cycle was used to explore processes of embedded agency resulting in structural change. CSOs are conceptualized as juggling resource constraints while balancing conflicting institutional logics: the communitarian logic promoted by resource controllers such as municipalities and Provincial Sport Organizations, and the individualist logic followed by CSO members. The results of the study demonstrate how CSOs compete for resources while balancing these institutional pressures and how when possible, CSOs manipulate institutional factors to gain legitimacy and contingent access to resources. In this competitive environment, LTAD represents a new institutional pressure. CSOs determine whether to adopt LTAD in part based on whether resource controllers signal that compliance will bring legitimacy and enhance resource access. When resource- controlling organizations introduce standards like LTAD intended to improve CSO program quality, the unintended result can be inter-CSO competition for legitimacy that can lead to the systematic privileging of large CSOs at the expense of smaller ones, driving professionalization and potentially increasing costs of sport participation

Modified Cellular Simultaneous Recurrent Networks with Cellular Particle Swarm Optimization

A cellular simultaneous recurrent network (CSRN) [1-11] is a neural network architecture that uses conventional simultaneous recurrent networks (SRNs), or cells in a cellular structure. The cellular structure adds complexity, so the training of CSRNs is far more challenging than that of conventional SRNs. Computer Go serves as an excellent test bed for CSRNs because of its clear-cut objective. For the training data, we developed an accurate theoretical foundation and game tree for the 2x2 game board. The conventional CSRN architecture suffers from the multi-valued function problem; our modified CSRN architecture overcomes the problem by employing ternary coding of the Go board\u27s representation and a normalized input dimension reduction. We demonstrate a 2x2 game tree trained with the proposed CSRN architecture and the proposed cellular particle swarm optimization

Orthogonal learning particle swarm optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness

Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

Spatial-temporal reasoning applications of computational intelligence in the game of Go and computer networks

Spatial-temporal reasoning is the ability to reason with spatial images or information about space over time. In this dissertation, computational intelligence techniques are applied to computer Go and computer network applications. Among four experiments, the first three are related to the game of Go, and the last one concerns the routing problem in computer networks. The first experiment represents the first training of a modified cellular simultaneous recurrent network (CSRN) trained with cellular particle swarm optimization (PSO). Another contribution is the development of a comprehensive theoretical study of a 2x2 Go research platform with a certified 5 dan Go expert. The proposed architecture successfully trains a 2x2 game tree. The contribution of the second experiment is the development of a computational intelligence algorithm calledcollective cooperative learning (CCL). CCL learns the group size of Go stones on a Go board with zero knowledge by communicating only with the immediate neighbors. An analysis determines the lower bound of a design parameter that guarantees a solution. The contribution of the third experiment is the proposal of a unified system architecture for a Go robot. A prototype Go robot is implemented for the first time in the literature. The last experiment tackles a disruption-tolerant routing problem for a network suffering from link disruption. This experiment represents the first time that the disruption-tolerant routing problem has been formulated with a Markov Decision Process. In addition, the packet delivery rate has been improved under a range of link disruption levels via a reinforcement learning approach --Abstract, page iv

SmartSwarm - A Multi-Agent Reinforcement Learning based Particle Swarm Optimization Algorithm

Particle Swarm Optimization is a renowned continuous optimization method that utilizes Swarm Intelligence to find solutions to complex non-linear optimization problems efficiently. Since its proposal, many developments have been put forward to improve its capabilities by enhancing the stochastic and tunable component of the algorithm. This thesis introduces SmartSwarm, a variant of Particle Swarm Optimization that utilizes Multi-Agent Reinforcement Learning to control the velocity of a swarm of particles. This framework has the capability of incorporating domain-specific information in the optimization process, as well as adapting a self-taught velocity function. We show how this framework has the ability to discover a velocity function to maximize the performance of the algorithm.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN