GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization

particle swarm optimization; swarm intelligence. In the Particle Swarm Optimization (PSO) algorithm, the expense of evaluating the objective function can make it difficult, or impossible, to use this approach effectively; reducing the number of necessary function evaluations would make it possible to apply the PSO algorithm more widely. Many function approximation techniques have been developed that address this issue, but an alternative to function approximation is function conservation. We describe GREEN-PSO (GR-PSO), an algorithm that, given a fixed number of function evaluations, conserves those function evaluations by probabilistically choosing a subset of particles smaller than the entire swarm on each iteration and allowing only those particles to perform function evaluations. The “surplus ” of function evaluations thus created allows a greater number of particles and/or iterations. In spite of the loss of information resulting from this more parsimonious use of function evaluations, GR-PSO performs as well as, or better than, the standard PSO algorithm on a set of six benchmark functions, both in terms of the rate of error reduction and the quality of the final solution.

Initial Experiments in Using Communication Swarms to Improve the Performance of Swarm Systems

Abstract. Swarm intelligence can provide robust, adaptable, scalable solutions to difficult problems. The distributed nature of swarm activity is the basis of these desirable qualities, but it also prevents swarm-based techniques from having direct access to global knowledge that could facilitate the task at hand. Our experiments indicate that a swarm system can use an auxiliary swarm, called a communication swarm, to create and distribute an approximation of useful global knowledge, without sacrificing robustness, adaptability, and scalability. We describe a communication swarm and validate its effectiveness on a simple problem.

APPSSAT: Approximate probabilistic planning using stochastic satisfiability

AbstractWe describe appssat, an anytime probabilistic contingent planner based on zander, a probabilistic contingent planner that operates by converting the planning problem to a stochastic satisfiability (Ssat) problem and solving that problem instead [S.M. Majercik, M.L. Littman, Contingent planning under uncertainty via stochastic satisfiability, Artificial Intelligence 147 (2003) 119–162]. The values of some of the variables in an Ssat instance are probabilistically determined; appssat considers the most likely instantiations of these variables (the most probable situations facing the agent) and attempts to construct an approximation of the optimal plan that succeeds under those circumstances, improving that plan as time permits. Given more time, less likely instantiations/situations are considered and the plan is revised as necessary. In some cases, a plan constructed to address a relatively low percentage of possible situations will succeed for situations not explicitly considered as well, and may return an optimal or near-optimal plan. We describe experimental results showing that appssat can find suboptimal plans in cases in which zander is unable to find the optimal (or any) plan. Although the test problems are small, the anytime quality of appssat means that it has the potential to efficiently derive suboptimal plans in larger, time-critical domains in which zander might not have sufficient time to calculate any plan. We also suggest further work needed to bring appssat closer to attacking real-world problems

The other world of Mark Twain: dreams and reality in his works

The development of an escape mechanism follows a pattern based on Twain\u27s ever-changing philosophy of life. This philosophy moved from a view of life as idyllic, through various stages, to a refusal to accept the unbeatable evils of life by refusing to accept their reality. The pattern itself is tracaeable in the development of Twain\u27s writings

Contingent planning under uncertainty via stochastic satisfiability

We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPs). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems. © 2003 Elsevier Science B.V. All rights reserved

Faster probabilistic planning through more efficient stochastic satisfiability problem encodings

The propositional contingent planner ZANDER solves finitehorizon, partially observable, probabilistic planning problems at state-of-the-art-speeds by converting the planning problem to a stochastic satisfiability (SSAT) problem and solving that problem instead (Majercik 2000). ZANDER obtains these results using a relatively inefficient SSAT encoding of the problem (a linear action encoding with classical frame axioms). We describe and analyze three alternative SSAT encodings for probabilistic planning problems: a linear action encoding with simple explanatory frame axioms, a linear action encoding with complex explanatory frame axioms, and a parallel action encoding. Results on a suite of test problems indicate that linear action encodings with simple explanatory frame axioms and parallel action encodings show particular promise, improving ZANDER’s efficiency by as much as three orders of magnitude

Steady state particle swarm

The following grant information was disclosed by the authors: Fundação para a Ciência e Tecnologia (FCT), Research Fellowship: SFRH/BPD/66876/2009. FCT PROJECT: UID/EEA/50009/2013. EPHEMECH: TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity. PROY-PP2015-06: Plan Propio 2015 UGR. CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada.This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.This work was supported by Fundação para a Ciência e Tecnologia (FCT) Research Fellowship SFRH/BPD/66876/2009 and FCT Project (UID/EEA/50009/2013), EPHEMECH (TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity), PROY-PP2015-06 (Plan Propio 2015 UGR), project CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada