Non Linear Blind Source Separation Using Different Optimization Techniques

The Independent Component Analysis technique has been used in Blind Source separation of non linear mixtures. The project involves the blind source separation of a non linear mixture of signals based on their mutual independence as the evaluation criteria. The linear mixer is modeled by the Fast ICA algorithm while the Non linear mixer is modeled by an odd polynomial function whose parameters are updated by four separate optimization techniques which are Particle Swarm Optimization, Real coded Genetic Algorithm, Binary Genetic Algorithm and Bacterial Foraging Optimization. The separated mixture outputs of each case was studied and the mean square error in each case was compared giving an idea of the effectiveness of each optimization technique

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions

The Application of Improved Bacteria Foraging Algorithm to the Optimization of Aviation Equipment Maintenance Scheduling

Taking the aviation equipment scheduled maintenance as a prototype, this paper improves a bionic global random search algorithm - bacteria foraging optimization algorithm to solve the task-scheduling problem. Inspired by gene mutation, the activity of bacteria is dynamically adjusted to make good bacteria more capable of action. In addition, a bacterial quorum sensing mechanism is established, which allows bacteria to guide their swimming routes by using their peer experience and enhance their global search capability. Its application to the engineering practice can optimize the scheduling of the maintenance process. It is of great application value in increasing the aviation equipment maintenance efficiency and the level of command automation. In addition, it can improve the resource utilization ratio to reduce the maintenance support cost

Reactive scheduling to treat disruptive events in the MRCPSP

Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well