Hardware/Software Co-design for Particle Swarm Optimization Algorithm

[[abstract]]This paper presents a hardware/software (HW/SW) co-design approach using SOPC technique and pipeline design method to improve the performance of particle swarm optimization (PSO) for embedded applications. Based on modular design architecture, a particle updating accelerator module via hardware implementation for updating velocity and position of particles and a fitness evaluation module implemented on a soft-cored processor for evaluating the objective functions are respectively designed and work closely together to accelerate the evolution process. Thanks to a flexible design, the proposed approach can tackle various optimization problems of embedded applications without the need for hardware redesign. To compensate the deficiency in generating truly random numbers by hardware implementation, a particle re-initialization scheme is also presented in this paper to further improve the execution performance of the PSO. Experiment results have demonstrated that the proposed HW/SW co-design approach to realize PSO is capable of achieving a high-quality solution effectively.[[conferencetype]]國際[[conferencedate]]20101010~20101013[[iscallforpapers]]Y[[conferencelocation]]Istanbul, Turke

Métodos discretos basados en quimiotaxis de bacterias y algoritmos genéticos para solucionar el problema de la distribución de planta en celdas de manufactura.

This paper presents the mono-objective and multi-objective solution to the cell manufacturing layout problem using two new discrete hybrid algorithms based on bacterial chemotaxis and genetic algorithms. The proposed models simultaneously solve the issues that constitute the problem of the layout of manufacturing cells: the formation of the cells and the inter- and intra-cell layout, considering the clustering of cells, and the cost of transportation and material handling. The performance of the proposals was evaluated with benchmark problems of manufacturing cells, traveling salesman problem and a multi-objective version of knapsack problem. The mono-objective results were compared with GA, BFOA and Bacterial-GA, while the multi-objective results were compared with well-known algorithms NSGA2 and SPEA2, obtaining better performances in both cases.Este trabajo presenta la solución mono-objetivo y multi-objetivo del problema de la distribución de planta en celdas de manufactura a través de dos nuevos algoritmos híbridos discretos basados en quimiotaxis de bacterias y en algoritmos genéticos. Los modelos propuestos resuelven simultáneamente los dos inconvenientes que constituyen el problema de la distribución de planta en celdas de manufactura: la formación de las celdas y la distribución de planta intra e inter celdas, considerando el agrupamiento de las celdas y el costo de transporte y manipulación de materiales. El desempeño de las propuestas se evaluó con problemas de prueba de distribución de planta de celdas de manufactura, agente viajero (TSP) y el caso multi-objetivo del problema de las mochilas. Los resultados mono-objetivo se compararon con AG, BFOA y Bacterial-GA, mientras que los resultados multi-objetivo se compararon con los reconocidos algoritmos NSGA2 y SPEA2 en los que se obtuvo un mejor desempeño en los dos casos

A novel hybrid teaching learning based multi-objective particle swarm optimization

How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to promote the diversity and improve search ability. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence

Study on the Structure Optimization and the Operation Scheme Design of a Double-Tube Once-Through Steam Generator

AbstractA double-tube once-through steam generator (DOTSG) consisting of an outer straight tube and an inner helical tube is studied in this work. First, the structure of the DOTSG is optimized by considering two different objective functions. The tube length and the total pressure drop are considered as the first and second objective functions, respectively. Because the DOTSG is divided into the subcooled, boiling, and superheated sections according to the different secondary fluid states, the pitches in the three sections are defined as the optimization variables. A multi-objective optimization model is established and solved by particle swarm optimization. The optimization pitch is small in the subcooled region and superheated region, and large in the boiling region. Considering the availability of the optimum structure at power levels below 100% full power, we propose a new operating scheme that can fix the boundaries between the three heat-transfer sections. The operation scheme is proposed on the basis of data for full power, and the operation parameters are calculated at low power level. The primary inlet and outlet temperatures, as well as flow rate and secondary outlet temperature are changed according to the operation procedure

Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants

pp. 185-204The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.S

EMCSO: An Elitist Multi-Objective Cat Swarm Optimization

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses thenon-dominated sorting method to findthe solutionsas close as to POFand crowding distance technique toobtain a uniform distribution among thenon-dominated solutions. Also, the algorithm is allowedto keep the elites of population in reproduction processand use an opposition-based learning method for population initialization to enhance the convergence speed.The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based onperformance measures of generational distance (GD), inverted GD, spread,and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem

Integrated methodology for supplier selection: the case of a sphygmomanometer manufacturer in Taiwan

Supplier selection is a critical multi-criterion decision-making activity for suc- cessful supply chain management. This study involved developing an integrated supplier selection methodology, which is constructed using analytic network process, data envelop- ment analysis, and multiple objective particle swarm optimization. The proposed integrated methodology can account for multiple supplier selection criteria and set boundaries on weight value for multiple objective data envelopment analysis inputs and outputs. To solve the data envelopment analysis model, a new algorithm based on multiple objective particle swarm optimization is introduced, which embeds with tabu list and group mechanisms, and then, it is found to be superior to the compared algorithms in solving performance on three test functions and the illustrative case. In addition, the proposed integrated method- ology was applied to a supplier selection problem of sphygmomanometer manufacturer in Taiwan to verify its applicability of decision-making process. The results show that the methodology can be implemented as an effective decision aid for supplier selection under multiple criteria with weight restrictions