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The Self-Organizing Map (SOM) is a well-known method for unsupervised learning. It can project high-dimensional data onto a low-dimensional topology map which makes it an efficient tool for visualization and dimensionality reduction. The Particle Swarm Optimization (PSO) is a swarm-intelligence meta-heuristic optimization algorithm based on the feeding behavior of a swarm of birds. In this paper, we have combined these two diverse approaches to form a PSO-SOM which is applied to clustering problems. The advantage of this method is the reduction in computational complexity and increase in clustering accuracy as demonstrated by the experimental results.

One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are equipped with Automatic Train Operation systems (ATO) additional difficulties are involved. Their particular features make it necessary to develop accurate models that optimize the combination of the ATO commands of each speed profile to be used by the traffic regulation system. These commands are transmitted to the train via encoded balises on the track with little channel capacity (bandwidth). Thus, only a few and discrete values of the commands can be sent and the solution space of every interstation is made up of a relatively small set of speed profiles. However, the new state-of-the-art of signalling technologies permit a better bandwidth resulting in an exponential solution space. This calls for new methods for the optimal design of the ATO speed profiles without an exhaustive simulation of all the combinations. A MOPSO algorithm (Multi Objective Particle Swarm Optimization) to obtain the consumption/time Pareto front based on the simulation of a train with a real ATO system is proposed. The algorithm is able even to take into account only the comfortable speed profiles of the solution space. The fitness of the Pareto front is verified by comparing it with a NSGA-II algorithm (non-dominated sorting genetic algorithm II) and with the real Pareto front. Further, it has been used to obtain the optimal speed profiles in a real line of the Madrid Underground. (C) 2014 Elsevier Ltd. All rights reserved.

Interactive Evolutionary Computational (IEC) algorithms have to be able to search robustly under the conditions of lower population number and fewer generations. This paper presents a novel IEC called interactive Ant System (iAS) which is interactively used by an user to determine the optimized combination of jacket, T-shirt, trousers and shoes. The iAS is the first of its kind in extending the well-known ACO to interactive optimization. Our experiments show that the iAS is capable of optimizing the user's requests in real time.

Interactive Evolutionary Computation (IEC) deals with the optimization of an objective function that is subjectively evaluated by repeated interaction with the user. The critical problem in IEC application areas is the load on the user because the iterative interaction between the human user and the system to evaluate the solutions produces stress and fatigue in the human user. Ant Colony Optimization (ACO) is a well-known Evolutionary Computation Algorithm. In this paper, we propose the Interactive Ant Colony Optimization (IACO), which is an extension of the ACO algorithm to the IEC application fields. The ACO algorithm has a parameter called evaporation rate which controls the speed of search convergence. If this parameter is tailored to the application problem, the IACO algorithms are able to accelerate the search and get the admissible solution. Owing to this control parameter, IACO has the potential of being a promising IEC algorithm. Our new algorithm called Simulated Breeding-based Interactive Ant System (SIAS) is fitted with Simulated Breeding as the evaluation-input method. To evaluate the performance of the proposed algorithm, we define the pseudo-IEC user based on the wellknown fitness landscape NK model and use it for the IEC simulation. The experimental results show that the performance of SIAS is superior to that of Interactive Genetic Algorithm (IGA) which is a typical IEC algorithm.

Software development projects are notorious for being completed behind schedule and over budget and for often failing to meet user requirements. A variety of cost estimation models have been proposed to predict development costs early in the lifecycle with the hope of managing the project well within time and budget. However, studies have reported rather high error rates of prediction even in the case of the well-established and widely acknowledged models. This study focuses on the improvement and fine-tuning of the COCOMO 81 and the Desharnais model through Feature Subset Selection, using the Artificial Immune System. Our research reconfirms the conventional data mining techniques and makes further improvement in the prediction accuracy. The AIS meta-heuristic approach is domain-independent and faster compared to the wrapper data mining method which is slow and heavily dependent on the domain experts' heuristics.

This paper deals with the performance modeling and the optimization of concurrent service systems. In large and complex service systems, asynchronous and concurrently occurring activities are common. Petri nets are ideal tools for modeling concurrent systems. However, Petri nets are lacking in time duration concept, in data collecting mechanism and in conjunctive logic on the preconditions of an event. These inherent limitations along with the state explosion problem severely restrict their scope of application. In this paper, we introduce the Client Server Petri net, which overcomes all these limitations. The Client Server Petri net is an extension of the Generalized Stochastic Petri net that allows greater flexibility in modeling and simulating concurrent systems. The total operational cost of service systems consists of service cost and waiting cost. The former is due to the hiring of service personnel, while the latter is due to the fact that customers weary of waiting may take their business somewhere else. The problem, in principle, can be formulated as a multi-objective optimization problem and then solved to obtain the Pareto-front. In this study, however, we formulate it as a single-objective optimization problem because optimization (minimization) of the total cost (service cost + waiting cost) is of paramount importance in economic models. Finding the optimal operational cost becomes a combinatorial optimization problem which we seek to minimize using the genetic algorithm, known for its robustness and versatility as an optimization meta-heuristic. We demonstrate the effectiveness of the novel Client Server Petri net model editor-simulator-optimizer with the practical example of an automobile purchase concurrent service system. (C) 2011 Elsevier B.V. All rights reserved.

Concurrent service systems are modeled using the Generalized Stochastic Petri Nets (GSPN) to account for the multiple asynchronous activities within the system. The simulated operation of the GSPN modeled system is then optimized using the Particle Swarm Optimization (PSO) meta-heuristic algorithm. The objective function consists of the service costs and the waiting costs. Service cost is the cost of hiring service-providing professionals, while waiting cost is the estimate of the loss to business as some customers might not be willing to wait for the service and may decide to go to the competing organizations. The optimization is subject to the management and to the customer satisfaction constraints. The tailor-made PSO is found to converge rapidly yielding optimum results for the operation of a practical concurrent service system. © Springer Science+Business Media B.V. 2010.

The development of software projects requires the co-ordination of the efforts of a team of professionals with varying skills. The major problem in software development is to assign the right personnel to the right job at the right time and at the right cost. In this study, we propose a skill-to-time model in which the task processing time varies in accordance with the level of skill possessed by the personnel assigned to the task. We cast the problem as a multi-objective optimization problem and simultaneously optimize the development cost and the development time using the Multi-Objective Particle Swarm Optimization algorithm. The optimization result is a well-defined and well-spread Pareto front representing the trade-off between development time and development cost.

Market research on services and waiting lends support to the fact that waiting for service is an undesirable phenomenon adversely affecting customer satisfaction and consequently business enterprises. The simulation optimization strategy described in this study addresses both the subjective as well as the objective elements in the patients' evaluation of healthcare services. The cost function simulates the average sojourn time of the patients in the healthcare system, whereas the fuzzy constraints quantitatively estimate the overall waiting and service experience of the patients. Minimization of the cost function (objective element in the patients' evaluation of service) subject to the fuzzy constraints (subjective elements in the patients' evaluation of service) leads to an increased patient satisfaction, increased efficiency of system operation and consequently increased profits. The proposed method makes a topological cum functional model of the healthcare system, simulates the operation via discrete event simulation and optimizes it using the Genetic Algorithm.

Concurrent service systems are modeled using the Generalized Stochastic Petri Nets (GSPN) to account for the multiple asynchronous activities within the system. The simulated operation of the GSPN modeled system is then optimized using the Particle Swarm Optimization (PSO) meta-heuristic algorithm. The objective function consists of the service costs and the waiting costs. Service cost is the cost of hiring service-providing professionals, while waiting cost is the estimate of the loss to business as some customers might not be willing to wait for the service and may decide to go to the competing organizations. The optimization is subject to the management and to the customer satisfaction constraints. The tailor-made PSO is found to converge rapidly yielding optimum results for the operation of a practical concurrent service system.