Particle Swarm Optimization: An efficient method for tracing periodic orbits in 3D galactic potentials

We propose the Particle Swarm Optimization (PSO) as an alternative method for locating periodic orbits in a three--dimensional (3D) model of barred galaxies. We develop an appropriate scheme that transforms the problem of finding periodic orbits into the problem of detecting global minimizers of a function, which is defined on the Poincar\'{e} Surface of Section (PSS) of the Hamiltonian system. By combining the PSO method with deflection techniques, we succeeded in tracing systematically several periodic orbits of the system. The method succeeded in tracing the initial conditions of periodic orbits in cases where Newton iterative techniques had difficulties. In particular, we found families of 2D and 3D periodic orbits associated with the inner 8:1 to 12:1 resonances, between the radial 4:1 and corotation resonances of our 3D Ferrers bar model. The main advantages of the proposed algorithm is its simplicity, its ability to work using function values solely, as well as its ability to locate many periodic orbits per run at a given Jacobian constant.Comment: 12 pages, 8 figures, accepted for publication in MNRA

Adaptive Memetic Particle Swarm Optimization with Variable Local Search Pool Size

We propose an adaptive Memetic Particle Swarm Optimization algorithm where local search is selected from a pool of different algorithms. The choice of local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of local search. Our study investigates whether the pool size affects the memetic algorithm’s performance, as well as the possible benefit of using the adaptive strategy against a baseline static one. For this purpose, we employed the memetic algorithms framework provided in the recent MEMPSODE optimization software, and tested the proposed algorithms on the Benchmarking Black Box Optimization (BBOB 2012) test suite. The obtained results lead to a series of useful conclusions

A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts

Copyright © 2005 Springer Verlag. The final publication is available at link.springer.com3rd International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005. ProceedingsBook title: Evolutionary Multi-Criterion OptimizationIn extending the Particle Swarm Optimisation methodology to multi-objective problems it is unclear how global guides for particles should be selected. Previous work has relied on metric information in objective space, although this is at variance with the notion of dominance which is used to assess the quality of solutions. Here we propose methods based exclusively on dominance for selecting guides from a non-dominated archive. The methods are evaluated on standard test problems and we find that probabilistic selection favouring archival particles that dominate few particles provides good convergence towards and coverage of the Pareto front. We demonstrate that the scheme is robust to changes in objective scaling. We propose and evaluate methods for confining particles to the feasible region, and find that allowing particles to explore regions close to the constraint boundaries is important to ensure convergence to the Pareto front

Power-Aware QoS Enhancement in Multihop DS-CDMA Visual Sensor Networks

Abstract-We propose a quality-driven method for network resource allocation with transmission power control in a multihop Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Network (WVSN). A multihop WVSN typically consists of source nodes that monitor different areas and relay nodes that retransmit recorded scenes. In order to achieve the best possible video quality at the receiver while consuming the least possible transmission power, we propose a joint optimization scheme that allocates the available resources among the nodes with respect to the imposed constraints. Moreover, we formulate a weighted bi-objective optimization problem and study the tradeoff between video quality and consumed transmission power. The simulation demonstrate that excessive transmission power is used when power control is omitted for a rather small quality gain for certain nodes

Searching for the Optimal Defence Expenditure: An Answer in the Context of the Greek – Turkish Arms Race.

This paper aims at evaluating the extent to which the defence expenditure of Greece and Cyprus given their arms race against Turkey in the context of the Integrated Defence Doctrine policy constitutes a burden feasible to bear. The evaluation takes place using an Optimal Control solution constrained by a model emphasising on Greek and Cypriot defence expenditure. Various experiments and scenarios have been tested leading to the general conclusion that the defence expenditure in both allied countries seems to be driving their economies beyond capacity limits. This, however, by no means justifies the one sided disarmament policy currently followed by Greece, since the long – term armament programmes pursued by Turkey, the role of which in this arms race has been proven as leading, leave very small room to the Greek and Cypriot sides to reduce their defence expenditures

Searching for the Optimal Defence Expenditure: An Answer in the Context of the Greek – Turkish Arms Race.

This paper aims at evaluating the extent to which the defence expenditure of Greece and Cyprus given their arms race against Turkey in the context of the Integrated Defence Doctrine policy constitutes a burden feasible to bear. The evaluation takes place using an Optimal Control solution constrained by a model emphasising on Greek and Cypriot defence expenditure. Various experiments and scenarios have been tested leading to the general conclusion that the defence expenditure in both allied countries seems to be driving their economies beyond capacity limits. This, however, by no means justifies the one sided disarmament policy currently followed by Greece, since the long – term armament programmes pursued by Turkey, the role of which in this arms race has been proven as leading, leave very small room to the Greek and Cypriot sides to reduce their defence expenditures

Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems

In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included

Multilocal programming and applications

Preprint versionMultilocal programming aims to identify all local minimizers of unconstrained or constrained nonlinear optimization problems. The multilocal programming theory relies on global optimization strategies combined with simple ideas that are inspired in deflection or stretching techniques to avoid convergence to the already detected local minimizers. The most used methods to solve this type of problems are based on stochastic procedures and a population of solutions. In general, population-based methods are computationally expensive but rather reliable in identifying all local solutions. In this chapter, a review on recent techniques for multilocal programming is presented. Some real-world multilocal programming problems based on chemical engineering process design applications are described.Fundação para a Ciência e a Tecnologia (FCT

A Multimodal Problem for Competitive Coevolution

Coevolutionary algorithms are a special kind of evolutionary algorithm with advantages in solving certain specific kinds of problems. In particular, competitive coevolutionary algorithms can be used to study problems in which two sides compete against each other and must choose a suitable strategy. Often these problems are multimodal - there is more than one strong strategy for each side. In this paper, we introduce a scalable multimodal test problem for competitive coevolution, and use it to investigate the effectiveness of some common coevolutionary algorithm enhancement techniques