Daniele Peri

Particle Swarm Optimization: efficient globally convergent modifications

In this paper we consider the Particle Swarm Optimization (PSO) algorithm [1], [2], in the class of Evolutionary Algorithms, for the solution of global optimization problems. We analyze a couple of issues aiming at improving both the effectiveness and the efficiency of PSO. In particular, first we recognize that in accordance with the results in [3], the initial points configuration required by the method, may be a crucial issue for the efficiency of PSO iteration. Therefore, a promising strategy to generate initial points is provided in the paper.

Simulation based design optimization of waterjet propelled Delft catamaran

The present work focuses on the application of simulation-based design for the resistance optimization of waterjet propelled Delft catamaran, using integrated computational and experimental fluid dynamics. A variable physics/variable fidelity approach was implemented wherein the objective function was evaluated using both low fidelity potential flow solvers with a simplified CFD waterjet model and high fidelity RANS solvers with discretized duct flow calculations. Both solvers were verified and validated with data for the original hull. The particle swarm optimizer was used for single speed optimization at Fr = 0.5, and genetic algorithms were used for multi speed optimization at Fr = 0.3, 0.5 and 0.7. The variable physics/variable fidelity approach was compared with high fidelity approach for the bare-hull shape optimization and it showed an overall CPU time reduction of 54% and converged to the same optimal design at Fr = 0.5. The multi-speed optimization showed design improvement at Fr = 0.5 and 0.7, but not at Fr = 0.3 since the design variables were obtained based on sensitivity analysis at Fr = 0.5. High fidelity simulation results for the optimized barehull geometry indicated 4% reduction in resistance and the optimized waterjet equipped geometry indicated 11% reduction in effective pump power required at self-propulsion. Verification was performed for the optimized hull form and its reduction in powering will be validated in forthcoming experimental campaign.

Global Optimization Algorithms in Naval Hydrodynamics

Abstract Several global optimization algorithms are discussed with results for a real ship design application. The focus is on the development of deterministic methods, which are preferred for their good theoretical properties and for the reduced number of objective function evaluations required.

Globally Convergent Hybridization of Particle Swarm Optimization Using Line Search-Based Derivative-Free Techniques

The hybrid use of exact and heuristic derivative-free methods for global unconstrained optimization problems is presented. Many real-world problems are modeled by computationally expensive functions, such as problems in simulation-based design of complex engineering systems. Objective-function values are often provided by systems of partial differential equations, solved by computationally expensive black-box tools. The objective-function is likely noisy and its derivatives are often not available. On the one hand, the use of exact optimization methods might be computationally too expensive, especially if asymptotic convergence properties are sought. On the other hand, heuristic methods do not guarantee the stationarity of their final solutions. Nevertheless, heuristic methods are usually able to provide an approximate solution at a reasonable computational cost, and have been widely applied to real-world simulation-based design optimization problems. Herein, an overall hybrid algorithm combining the appealing properties of both exact and heuristic methods is discussed, with focus on Particle Swarm Optimization (PSO) and line search-based derivative-free algorithms. The theoretical properties of the hybrid algorithm are detailed, in terms of limit points stationarity. Numerical results are presented for a specific test function and for two real-world optimization problems in ship hydrodynamics.

Penalty function approaches for ship multidisciplinary design optimisation (MDO)

This paper focuses on the solution of difficult multidisciplinary optimisation formulations arising in ship design. The latter schemes are by nature the result of the interaction among several optimisation problems. Each optimisation problem summarises the issues related to a specific aspect (discipline) of the formulation, and it may be hardly solved by stand-alone methods which ignore the other disciplines. This usually yields very challenging numerical optimisation problems, due to the simultaneous solution of different schemes. In particular, in our ship design applications we stress the strong interaction between fluid-dynamics and optimisation, in order to get remarkable achievements. The ordinary stand-alone methods from mathematical programming prove to be often unsatisfactory on the latter multidisciplinary problems. This scenario requires a specific integration of both fluid-dynamics and optimisation, where constrained optimisation schemes frequently arise. We give evidence that the proper use of penalty methods, combined with global optimisation techniques, may both be a theoretically correct approach, and may yield a fruitful class of techniques for the solution of multidisciplinary problems. We provide numerical results with different penalty functions, over difficult multidisciplinary formulations from ship design. Here, the introduction of penalty methods proved to be a valuable tool since feasibility issues strongly affect the formulation. [Received 16 January 2009; Revised 4 May 2010; Revised 15 August 2011; Accepted 30 September 2011]

Urban Air Quality Monitoring Using Vehicular Sensor Networks

The quality of air is a major concern in modern cities as pollutants have been demonstrated to have significant impact on human health. Networks of fixed monitoring stations have been deployed in urban areas to provide authorities with data to define and enforce dynamically policies to reduce pollutants, for instance by issuing traffic regulation measures. However, fixed networks require careful placement of monitoring stations to be effective. Moreover, changes in urban arrangement, activities, or regulations may affect considerably the monitoring model, especially when budget constraints prevent from relocating stations or adding new ones to the network. In this chapter we discuss a different approach to environmental monitoring through mobile monitoring devices implementing a Vehicular Sensor Network (VSN) to be deployed on the public transport bus fleet of Palermo.

High-Fidelity models in global optimization

This work presents a Simulation Based Design environment based on a Global Optimization (GO) algorithm for the solution of optimum design problems. The procedure, illustrated in the framework of a multiobjective ship design optimization problem, make use of high-fidelity, CPU time expensive computational models, including a free surface capturing RANSE solver. The use of GO prevents the optimizer to be trapped into local minima. The optimization is composed by global and local phases. In the global stage of the search, a few computationally expensive simulations are needed for creating surrogate models (metamodels) of the objective functions. Tentative design, created to explore the design variable space are evaluated with these inexpensive analytical approximations. The more promising designs are clustered, then locally minimized and eventually verified with high-fidelity simulations. New exact values are used to improve the metamodels and repeated cycles of the algorithm are performed. A Decision Maker strategy is finally adopted to select the more promising design.

A Lightweight Middleware Platform for Distributed Computing on Wireless Sensor Networks

Abstract The peculiar features of Wireless Sensor Networks (WSNs) suggest to exploit the distributed computing paradigm to perform complex tasks in a collaborative manner, in order to overcome the constraints related to sensor nodes limited capabilities. In this context, we describe a lightweight middleware platform to support the development of distributed applications on WSNs. The platform provides just a minimal general-purpose software layer, while the application components, including communication and processing algorithms, as well as the exchanged data, are described symbolically, with neither preformed syntax nor strict distinction between data and code. Our approach allows for interactive development of applications on each node, and requires no cross-compilation, a common practice that makes the development of WSN applications rigid and time-consuming. This way, tasks and behavior of each node can be modified at runtime, even after the network deployment, by sending the node executable code.

GAIML: A new language for verbal and graphical interaction in chatbots

Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and speech processing modules. However the interaction between the system and the user is only performed through textual areas for inputs and replies. An interaction able to add to natural language also graphical widgets could be more effective. On the other side, a graphical interaction involving also the natural language can increase the comfort of the user instead of using only graphical widgets. In many applications multi-modal communication must be preferred when the user and the system have a tight and complex interaction. Typical examples are cultural heritages applications (intelligent museum guides, picture browsing) or systems providing the user with integrated information taken from different and heterogenous sources as in the case of the iGoogle™ interface. We propose to mix the two modalities (verbal and graphical) to build systems with a reconfigurable interface, which is able to change with respect to the particular application context. The result of this proposal is the Graphical Artificial Intelligence Markup Language (GAIML) an extension of AIML allowing merging both interaction modalities. In this context a suitable chatbot system called Graphbot is presented to support this language. With this language is possible to define personalized interface patterns that are the most suitable ones in relation to the data types exchanged between the user and the system according to the context of the dialogue.

A Proposal of PSO Particles' Initialization for Costly Unconstrained Optimization Problems: ORTHOinit

A proposal for particles’ initialization in PSO is presented and discussed, with focus on costly global unconstrained optimization problems. The standard PSO iteration is reformulated such that the trajectories of the particles are studied in an extended space, combining particles’ position and speed. To the aim of exploring effectively and efficiently the optimization search space since the early iterations, the particles are initialized using sets of orthogonal vectors in the extended space (orthogonal initialization, ORTHOinit). Theoretical derivation and application to a simulation-based optimization problem in ship design are presented, showing the potential benefits of the current approach.