A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

Multiobjective Particle Swarm Approach for the Design of a Brushless DC Wheel Motor

The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. Particle swarm optimization (PSO) is one of the modern metaheuristics of swarm intelligence, which can be effectively used to solve nonlinear and non-continuous optimization problems. The basic principle of PSO algorithm is formed on the assumption that potential solutions (particles) will be flown through hyperspace with acceleration towards more optimum solutions. Each particle adjusts its flying according to the flying experiences of both itself and its companions using equations of position and velocity. During the process, the coordinates in hyperspace associated with its previous best fitness solution and the overall best value attained so far by other particles within the group are kept track and recorded in the memory. In recent years, PSO approaches have been successfully implemented to different problem domains with multiple objectives. In this paper, a multiobjective PSO approach, based on concepts of Pareto optimality, dominance, archiving external with elite particles and truncated Cauchy distribution, is proposed and applied in the design with the constraints presence of a brushless DC (Direct Current) wheel motor. Promising results in terms of convergence and spacing performance metrics indicate that the proposed multiobjective PSO scheme is capable of producing good solutions.

Swarm Intelligence applied in synthesis of hunting strategies in a three-dimensional environment

Systems of distributed artificial intelligence can be powerful tools in a wide variety of practical applications. Its most surprising characteristic, the emergent behavior, is also the most answerable for the difficulty in. projecting these systems. This work proposes a tool capable to beget individual strategies for the elements of a multi-agent system and thereof providing to the group means on obtaining wanted results, working in a coordinated and cooperative manner as well. As an application example, a problem was taken as a basis where a predators` group must catch a prey in a three-dimensional continuous ambient. A synthesis of system strategies was implemented of which internal mechanism involves the integration between simulators by Particle Swarm Optimization algorithm (PSO), a Swarm Intelligence technique. The system had been tested in several simulation settings and it was capable to synthesize automatically successful hunting strategies, substantiating that the developed tool can provide, as long as it works with well-elaborated patterns, satisfactory solutions for problems of complex nature, of difficult resolution starting from analytical approaches. (c) 2007 Elsevier Ltd. All rights reserved.

Nestmate recognition in a Neotropical swarm-founding wasp: no effect of seasonality on tolerance of alien conspecifics

Previous study revealed that the swarm-founding wasp Polybia paulista is accurately able to distinguish nestmates from non-nestmates in the summer. However, the risk of accepting alien intruders is considered to be low in winter colonies, and additionally brood production is limited in 30-40% of colonies during the winter in this species. Thus, it is expected that colonies might lower their acceptance threshold and accept some conspecific wasps from alien colonies in winter. We conducted field experiments to examine tolerance of conspecific (nestmate and non-nestmate) females in winter. In contrast to our prediction, our colonies did not accept any individuals from alien colonies. We suggest that P. paulista exhibits the colony-specific acceptance threshold in winter, and colonies that produced brood in their nests may have raised the acceptance threshold even if the risk of accepting alien intruders is low in winter.

Worker demography in a large-colony, swarm-founding wasp

Neotropical swarm-founding wasps build nests enclosed in a covering envelope, which makes it difficult to count individual births and deaths. Thus, knowledge of worker demography is very limited for swarm-founding species compared with that for independent-founding species. In this study, we explored the worker demography of the swarm-founding wasp Polybia paulista, the colony size of which usually exceeds several thousand adults. We considered each wasp colony as an open-population and estimated the survival probability, recruitment rate, and population size of workers using the developments of the Cormack-Jolly-Seber model. We found that capture probability varied considerably among the workers, probably due to age polyethism and/or task specialization. The daily survival rate of workers was high (around 0.97) throughout the season and was not related to the phase of colony development. On the other hand, the recruitment rate ranged from 0 to 0.37, suggesting that worker production was substantially less important than worker survival in determining worker population fluctuations. When we compared survival rates among worker groups of one colony, the mean daily survival rate was lower for founding workers than for progeny workers and tended to be higher in progeny workers that emerged in winter. These differences in survivorship patterns among worker cohorts would be related to worker foraging activity and/or level of parasitism.

Nest site selection by a neotropical swarm-founding wasp: seasonal alternation of nest orientation

Nest orientation in social insects has been intensively studied in warmer and cooler climates, particularly in the northern hemisphere. Previous studies have consistently shown that species subjected to these climatic conditions prefer to select mostly southern locations where the nests can gain direct sunlight. However, very little is known on nest orientation in tropical and subtropical social insects. We studied nest orientations initiated by swarms throughout a year in a Brazilian swarm-founding wasp, Polybia paulista von Ihering (Hymenoptera: Polistinae). Swarms selected various orientations as nest sites, but there was a particular trend in that swarms in the winter period (May-August) preferred to build northward-facing nests. This preference is opposite from that of social wasps observed in the northern hemisphere. Colonies of this species can potentially last for many years with continuous nesting, but nesting activities of colonies during the winter are severely limited due to cool temperature and a shortened day length. Northward-facing nests are warmer through the gain of direct solar heat during the winter period; consequently, choosing northward-facing sites may be advantageous for swarms in terms of a shortened brood development and shortened time needed to increase metabolic rates during warm-up for flight.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Cooperative scheduling system with emergent swarm based behavior

This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective – global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization.