Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima

Copyright @ Elsevier Inc. All rights reserved.Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based force-imitated PSO (NN-FPSO) algorithm, each particle explores the promising regions where it resides under the composite forces produced by the “near-neighbor attractor” and “near-neighbor repeller”, which are selected from the set of memorized personal best positions and the current swarm based on the principles of “superior-and-nearer” and “inferior-and-nearer”, respectively. These two forces pull and push a particle to search for the nearby optimum. Hence, particles can simultaneously locate multiple optima quickly and precisely. Experiments are carried out to investigate the performance of NN-FPSO in comparison with a number of state-of-the-art PSO algorithms for locating multiple optima over a series of multimodal benchmark test functions. The experimental results indicate that the proposed NN-FPSO algorithm can efficiently locate multiple optima in multimodal fitness landscapes.This work was supported in part by the Key Program of National Natural Science Foundation (NNSF) of China under Grant 70931001, Grant 70771021, and Grant 70721001, the National Natural Science Foundation (NNSF) of China for Youth under Grant 61004121, Grant 70771021, the Science Fund for Creative Research Group of NNSF of China under Grant 60821063, the PhD Programs Foundation of Ministry of Education of China under Grant 200801450008, and in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1 and Grant EP/E060722/2

Particle swarm optimization with a leader and followers

Referring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO

Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

Comparing the impact of environmental factors during very high gravity brewing fermentations

The impact of the initial dissolved oxygen, fermentation temperature, wort concentration and yeast pitching rate on the major fermentation process responses were evaluated by full factorial design and statistical analysis by JMP 5.01 (SAS software) software. Fermentation trials were carried out in 2L-EBC tall tubes using an industrial lager brewing yeast strain. The yeast viability, ethanol production, apparent extract and real degree of fermentation were monitored. The results obtained demonstrate that very high gravity worts at 22°P can be fermented in the same period of time as a 15°P wort, by raising the temperature to 18°C, the oxygen level to about 22 ppm, and increasing the pitching rate to 22 × 106 cell/mL. When diluting to obtain an 11.5°P beer extract, the volumetric brewing capacity increased 91% for the 22°P wort fermentation and 30% using the 15°P wort. After dilution, the fermentation of the 22°P wort resulted in a beer with higher esters levels, primarily the compound ethyl acetate.(undefined

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Monitoring of the primary drying of a lyophilization process in vials

An innovative and modular system (LyoMonitor) for monitoring the primary drying of a lyophilization process in vials is illustrated: it integrates some commercial devices (pressure gauges, moisture sensor and mass spectrometer), an innovative balance and a manometric temperature measurement system based on an improved algorithm (DPE) to estimate sublimating interface temperature and position, product temperature profile, heat and mass transfer coefficients. A soft-sensor using a multipoint wireless thermometer can also estimate the previous parameters in a large number of vials. The performances of the previous devices for the determination of the end of the primary drying are compared. Finally, all these sensors can be used for control purposes and for the optimization of the process recipe; the use of DPE in a control loop will be shown as an exampl

Gravitational Swarm Optimizer for Global Optimization

In this article, a new meta-heuristic method is proposed by combining particle swarm optimization (PSO) and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the article. The e�fficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs e�fficiently with good results as a constrained optimizer

Prediction of Confidence Limits for Diacetyl Concentration During Beer Fermentation

International audienceA dynamic model for diacetyl production and reduction was developed based on experimental data from 14 laboratory-scale (15-L) lager beer fermentations carried out in various conditions of temperature (10–16°C), top pressure (50−850 mbar), initial yeast concentration (5−20 million cells per milliliter) and initial wort gravity (1,036−1,099 g/L). Uncertainties due to measurement errors, model parameters, and batch-to-batch variability were described in a probabilistic framework. The model predicts a probability distribution for the final diacetyl concentration from which a median value and an upper boundary, at a specified confidence level, are derived. It is demonstrated that in-line diacetyl measurements at early stages of fermentation greatly reduce the uncertainty about the final diacetyl level in each specific batch.Predicción de los Límites de Confianza para la Concentración de Diacetilo en Cerveza Durante la Fermentación Se desarrolló un modelo dinámico para la producción y reducción de diacetilo durante la fermentación, basado en datos experimentales de 14 fermentaciones en el laboratorio (15 L) con cerveza lager; estas fermentaciones se realizaron en diferentes condiciones de temperatura (10-16°C), sobrepresión (50–850 mbar), concentración inicial de levadura (5–20 millones de células por mililitro) y densidad inicial del mosto (1,036–1,099 g/L). Las incertidumbres debido a los errores de medición, los parámetros del modelo y la variabilidad de lote a lote, fueron descritas en un marco probabilístico. El modelo pronostica una distribución de probabilidad para la concentración final del diacetilo, de cual se deriva un valor de la mediana y del límite superior de la concentración, a un nivel de confianza especificada. Se demuestra que las mediciones de diacetilo en línea en las fases iniciales de la fermentación reducen de manera significativa la incertidumbre de la concentración final de diacetilo de cada lote

Reliable estimation of the key variables and of their rates of change in the alcoholic fermentation

International audienceThe paper establishes a rigorous probabilistic framework for the reconciliation of apparently conflicting data from various physical and chemical measurements related to the key biological variables of alcoholic fermentation: the ethanol and the residual sugar concentrations. The analysis is carried out on a database consisting of 15 beer fermentation experiments, for which off-line determinations of ethanol concentration, fermentable sugar concentration, wort density and refractive index are available, as well as on-line records of evolved CO2. The basic reconciliation method uses mass balance and monotonicity constraints derived from the biological knowledge of the fermentation process. In order to provide interpolated values and rate estimates, smoothness requirements are added. The reconciliation procedure gives more reliable estimates than any given measurement, detects outliers, helps fixing problems in the experimental setting and is also applicable on line

An Interactive Tool for the Optimization of Freeze-Drying Cycles Based on Quality Criteria

International audienceAmong existing dehydration methods, freeze-drying has unique benefits for the stabilization and preservation of biological activity of pharmaceutical products but remains an expensive and time-consuming process. A user-friendly software tool was developed, allowing for interactive selection of process operating condition profiles in order to maximize process productivity while insuring product quality preservation. The software is based on a dynamic, one-dimensional heat and mass transfer model, which can accurately represent both the primary and secondary drying stages and the gradual transition between them. The model was validated in a wide range of operating conditions: − 25 to + 25°C shelf temperature and 10 to 34 Pa total pressure. By comparing a reference sucrose solution with a formulated pharmaceutical product containing polyvinylpyrrolidone (PVP), it is shown that controlling product properties such as glass transition temperature and sorption isotherm can reduce the minimum achievable cycle duration by 12 h (33%)