Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit

This paper describes a novel initialization for Deterministic Particle Swarm Optimization (DPSO), based on choosing specific dense initial positions and velocities for particles. This choice tends to induce orthogonality of particles' trajectories, in the early iterations, in order to better explore the search space. Our proposal represents an improvement, by the same authors, of the theoretical analysis on a previously proposed PSO reformulation, namely the initialization ORTHOinit. A preliminary experience on constrained Portfolio Selection problems confirms our expectations

Particle swarm optimization : understanding order-2 stability guarantees

This paper’s primary aim is to provide clarity on which guarantees about particle stability can actually be made. The particle swarm optimization algorithm has undergone a considerable amount of theoretical analysis. However, with this abundance of theory has come some terminological inconstancies, and as a result it is easy for a practitioner to be misguided by overloaded terminology. Specifically, the criteria for both order-1 and order-2 stability are well studied, but the exact definition of order-2 stability is not consistent amongst researchers. A consequence of this inconsistency in terminology is that the existing theory may in fact misguide practitioners instead of assisting them. In this paper it is theoretically and empirically demonstrated which practical guarantees can in fact be made about particle stability. Specifically, it is shown that the definition of order-2 stability which accurately reflects PSO behavior is that of convergence in second order moment to a constant, and not to zero.http://link.springer.combookseries/5582020-03-30hj2020Computer Scienc

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 simulationbased 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 not provided. 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 test function and for two real-world optimization problems in ship hydrodynamics.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 simulationbased 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

Gaussian-valued particle swarm optimization

This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://link.springer.combookseries/5582019-10-03hj2018Computer Scienc

A Distributed Multi-level PSO Control Algorithm for Autonomous Underwater Vehicles

This paper presents a distributed control technique based on the Particle Swarm Optimization algorithm and able to drive in unknown environments a group of autonomous robots to a common target point. In this paper, we consider in particular the case of underwater vehicles. The algorithm is able to deal with complex scenarios, frequently found in benthic exploration as e.g. in presence of obstacles, caves and tunnels, and to consider the case of a mobile target. Moreover, asynchronous data exchange and dynamic communication topologies are considered. Simulations results are provided to show the features of the proposed approach

A novel approach for studying the indoor dispersion of aroma through computational fluid dynamics

We propose a mechanistic modelling approach for studying the indoor dispersion of aroma compounds which are released from, for instance, food products. The approach combines the indoor velocity field with a release model for aroma compounds. The release mass flux is expressed as a function of key variables such as mass transfer and gas-liquid partition coefficients, and the source geometry. The transport properties of ambient air are assumed to be independent of the aroma concentration; hence release and dispersion problems can be solved separately. First, the velocity field is obtained as solution of the fluid flow problem through computational fluid dynamics (CFD). The turbulent velocity field is then used to predict the time evolution of concentration of an aroma compound released by a constant rate source, in an initially aroma-free environment. These results are interpreted in terms of a step response function. The aroma concentration as a function of time is finally estimated by convolving the possibly time-varying release mass flux and the response function associated with the position of interest. The modelling approach is flexible and computationally effective, since different release models as well as the release of distinct aroma compounds can be directly studied by taking into account a same velocity field, without any additional CFD simulation. The validity of the approach is assessed from measurements of aroma concentration in a 140m3 room, under constant release mass flux. The approach is also illustrated for a case where the release mass flux is not constant in time. © 2013 John Wiley & Sons, Ltd

Estimation of kinetic reaction constants:exploiting reboot strategies to improve PSO’s performance

\u3cp\u3eThe simulation and analysis of mathematical models of biological systems require a complete knowledge of the reaction kinetic constants. Unfortunately, these values are often difficult to measure, but they can be inferred from experimental data in a process known as Parameter Estimation (PE). In this work, we tackle the PE problem using Particle Swarm Optimization (PSO) coupled with three different reboot strategies, which aim to reinitialize particle positions to avoid local optima. In particular, we highlight the better performance of PSO coupled with the reboot strategies with respect to standard PSO. Finally, since the PE requires a huge number of simulations at each iteration of PSO, we exploit cupSODA, a GPU-powered deterministic simulator, which performs all simulations and fitness evaluations in parallel.\u3c/p\u3