Cuckoo Search Inspired Hybridization of the Nelder-Mead Simplex Algorithm Applied to Optimization of Photovoltaic Cells

A new hybridization of the Cuckoo Search (CS) is developed and applied to optimize multi-cell solar systems; namely multi-junction and split spectrum cells. The new approach consists of combining the CS with the Nelder-Mead method. More precisely, instead of using single solutions as nests for the CS, we use the concept of a simplex which is used in the Nelder-Mead algorithm. This makes it possible to use the flip operation introduces in the Nelder-Mead algorithm instead of the Levy flight which is a standard part of the CS. In this way, the hybridized algorithm becomes more robust and less sensitive to parameter tuning which exists in CS. The goal of our work was to optimize the performance of multi-cell solar systems. Although the underlying problem consists of the minimization of a function of a relatively small number of parameters, the difficulty comes from the fact that the evaluation of the function is complex and only a small number of evaluations is possible. In our test, we show that the new method has a better performance when compared to similar but more compex hybridizations of Nelder-Mead algorithm using genetic algorithms or particle swarm optimization on standard benchmark functions. Finally, we show that the new method outperforms some standard meta-heuristics for the problem of interest

Improved Self-Adaptive Control Parameters in Differential Evolution Algorithm for Complex Numerical Optimization

芝浦工業大学201

Improved versions of the bees algorithm for global optimisation

This research focuses on swarm-based optimisation algorithms, specifically the Bees Algorithm. The Bees Algorithm was inspired by the foraging behaviour of honey bees in nature. It employs a combination of exploration and exploitation to find the solutions of optimisation problems. This thesis presents three improved versions of the Bees Algorithm aimed at speeding up its operation and facilitating the location of the global optimum. For the first improvement, an algorithm referred to as the Nelder and Mead Bees Algorithm (NMBA) was developed to provide a guiding direction during the neighbourhood search stage. The second improved algorithm, named the recombination-based Bees Algorithm (rBA), is a variant of the Bees Algorithm that utilises a recombination operator between the exploited and abandoned sites to produce new candidates closer to optimal solutions. The third improved Bees Algorithm, called the guided global best Bees Algorithm (gBA), introduces a new neighbourhood shrinking strategy based on the best solution so far for a more effective exploitation search and develops a new bee recruitment mechanism to reduce the number of parameters. The proposed algorithms were tested on a set of unconstrained numerical functions and constrained mechanical engineering design problems. The performance of the algorithms was compared with the standard Bees Algorithm and other swarm based algorithms. The results showed that the improved Bees Algorithms performed better than the standard Bees Algorithm and other algorithms on most of the problems tested. Furthermore, the algorithms also involve no additional parameters and a reduction on the number of parameters as well

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Training Single Walled Carbon Nanotube based Materials to perform computation

This thesis illustrates the use of Single Walled Carbon Nanotube based materials for the solution of various computational problems by using the process of computer controlled evolution. The study aims to explore and identify three dimensions of a form of unconventional computing called, `Evolution-in-materio'. First, it focuses on identifying suitable materials for computation. Second, it explores suitable methods, i.e. optimisation and evolutionary algorithms to train these materials to perform computation. And third, it aims to identify suitable computational problems to test with these materials. Different carbon based materials, mainly single walled carbon nano-tubes with their varying concentrations in polymers have been studied to be trained for different computational problems using the principal of `evolution-in-materio'. The conductive property of the materials is used to train these materials to perform some meaningful computation. The training process is formulated as an optimisation problem with hardware in loop. It involves the application of an external stimuli (voltages) on the material which brings changes in its electrical properties. In order to train the material for a specific computational problem, a large number of configuration signals need to be tested to find the one that transforms the incident signal in such a way that a meaningful computation can be extracted from the material. An evolutionary algorithm is used to identify this configuration data and using a hardware platform, this data is transformed into incident signals. Depending on the computational problem, the specific voltages signals when applied at specific points on to the material, as identified by an evolutionary algorithm, can make the material behave as a Logic gate, a tone discriminator or a data classifier. The problem is implemented on two types of hardware platforms, one a more simple implementation using mbed ( a micro- controller) and other is a purpose-built platform for `Evolution-in-materio" called Mecobo. The results of this study showed that the single walled carbon nanotube composites can be trained to perform simple computational tasks (such as tone discriminator, AND, OR logic gates and a Half adder circuit), as well as complex computational problems such as Full Adder circuit and various binary and multiple class machine learning problems. The study has also identified the suitability of using evolutionary algorithms such as Particle Swarm Optimisation algorithm (PSO) and Differential evolution for finding solutions of complex computational problems such as complex logic gates and various machine learning classification problems. The implementation of classification problem with the carbon nanotube based materials also identified the role of a classifier. It has been found that K-nearest neighbour method and its variant kNN ball tree algorithm are more suitable to train carbon nanotube based materials for different classification problems. The study of varying concentrations of single walled carbon nanotubes in fixed polymer ratio for the solution of different computational problems provided an indication of the link between single walled carbon nanotubes concentration and ability to solve computational problem. The materials used in this study showed stability in the results for all the considered computational problems. These material systems can compliment the current electronic technology and can be used to create a new type of low energy and low cost electronic devices. This offers a promising new direction for evolutionary computation

Vibration control in buildings under seismic excitation using optimized tuned mass dampers

Earthquakes can cause vibration problems in many types of structures, generating large displacements. The interstory drift is a design criterion very used in seismic analysis and the structural control is an alternative to reduce these displacements and improve the performance of these structures adapting them to the imposed criteria. TMD is a device widely used due to the simple principle of operation and many successful applications in real life practice. This paper investigates the use of optimized TMD for reduction of maximum horizontal displacement at the top floor and interstory drift of a steel building under seismic excitation considering three scenarios: single TMD at the top floor; MTMD horizontally arranged at the top floor; and MTMD vertically arranged on the structure. By a metaheuristic optimization algorithm, the parameters and positions of the devices are obtained. Three real and one artificial earthquakes are employed in the simulations. The results showed that all proposed scenarios are efficient in reducing top floor response and interstory drift to values below of the interstory drift limits allowed by the standard code consulted. However, Scenario 2 presented the best reduction for the top displacement and interstory drift to the critical floor for the worst earthquake considered

Exact and non-exact procedures for solving the response time variability problem (RTVP)

Premi extraordinari doctorat curs 2009-2010, àmbit d’Enginyeria IndustrialCuando se ha de compartir un recurso entre demandas (de productos, clientes, tareas, etc.) competitivas que requieren una atención regular, es importante programar el derecho al acceso del recurso de alguna forma justa de manera que cada producto, cliente o tarea reciba un acceso al recurso proporcional a su demanda relativa al total de las demandas competitivas. Este tipo de problemas de secuenciación pueden ser generalizados bajo el siguiente esquema. Dados n símbolos, cada uno con demanda di (i = 1,...,n), se ha de generar una secuencia justa o regular donde cada símbolo aparezca di veces. No existe una definición universal de justicia, ya que puede haber varias métricas razonables para medirla según el problema específico considerado. En el Problema de Variabilidad en el Tiempo de Respuesta, o Response Time Variability Problem (RTVP) en inglés, la injusticia o irregularidad de una secuencia es medida como la suma, para todos los símbolos, de sus variabilidades en las distancias en que las copias de cada símbolo son secuenciados. Así, el objetivo del RTVP es encontrar la secuencia que minimice la variabilidad total. En otras palabras, el objetivo del RTVP es minimizar la variabilidad de los instantes en que los productos, clientes o trabajos reciben el recurso necesario. Este problema aparece en una amplia variedad de situaciones de la vida real; entre otras, secuenciación en líneas de modelo-mixto bajo just-in-time (JIT), en asignación de recursos en sistemas computacionales multi-hilo como sistemas operativos, servidores de red y aplicaciones mutimedia, en el mantenimiento periódico de maquinaria, en la recolección de basura, en la programación de comerciales en televisión y en el diseño de rutas para agentes comerciales con múltiples visitas a un mismo cliente. En algunos de estos problemas la regularidad no es una propiedad deseable por sí misma, si no que ayuda a minimizar costes. De hecho, cuando los costes son proporcionales al cuadrado de las distancias, el problema de minimizar costes y el RTVP son equivalentes. El RTVP es muy difícil de resolver (se ha demostrado que es NP-hard). El tamaño de las instancias del RTVP que pueden ser resueltas óptimamente con el mejor método exacto existente en la literatura tiene un límite práctico de 40 unidades. Por otro lado, los métodos no exactos propuestos en la literatura para resolver instancias mayores consisten en heurísticos simples que obtienen soluciones rápidamente, pero cuya calidad puede ser mejorada. Por tanto, los métodos de resolución existentes en la literatura son insuficientes. El principal objetivo de esta tesis es mejorar la resolución del RTVP. Este objetivo se divide en los dos siguientes subobjetivos : 1) aumentar el tamaño de las instancias del RTVP que puedan ser resueltas de forma óptima en un tiempo de computación práctico, y 2) obtener de forma eficiente soluciones lo más cercanas a las óptimas para instancias mayores. Además, la tesis tiene los dos siguientes objetivos secundarios: a) investigar el uso de metaheurísticos bajo el esquema de los hiper-heurísticos, y b) diseñar un procedimiento sistemático y automático para fijar los valores adecuados a los parámetros de los algoritmos. Se han desarrollado diversos métodos para alcanzar los objetivos anteriormente descritos. Para la resolución del RTVP se ha diseñado un método exacto basado en la técnica branch and bound y el tamaño de las instancias que pueden resolverse en un tiempo práctico se ha incrementado a 55 unidades. Para instancias mayores, se han diseñado métodos heurísticos, metaheurísticos e hiper-heurísticos, los cuales pueden obtener soluciones óptimas o casi óptimas rápidamente. Además, se ha propuesto un procedimiento sistemático y automático para tunear parámetros que aprovecha las ventajas de dos procedimientos existentes (el algoritmo Nelder & Mead y CALIBRA).When a resource must be shared between competing demands (of products, clients, jobs, etc.) that require regular attention, it is important to schedule the access right to the resource in some fair manner so that each product, client or job receives a share of the resource that is proportional to its demand relative to the total of the competing demands. These types of sequencing problems can be generalized under the following scheme. Given n symbols, each one with demand di (i = 1,...,n), a fair or regular sequence must be built in which each symbol appears di times. There is not a universal definition of fairness, as several reasonable metrics to measure it can be defined according to the specific considered problem. In the Response Time Variability Problem (RTVP), the unfairness or the irregularity of a sequence is measured by the sum, for all symbols, of their variabilities in the positions at which the copies of each symbol are sequenced. Thus, the objective of the RTVP is to find the sequence that minimises the total variability. In other words, the RTVP objective is to minimise the variability in the instants at which products, clients or jobs receive the necessary resource. This problem appears in a broad range of real-world areas. Applications include sequencing of mixed-model assembly lines under just-in-time (JIT), resource allocation in computer multi-threaded systems such as operating systems, network servers and media-based applications, periodic machine maintenance, waste collection, scheduling commercial videotapes for television and designing of salespeople's routes with multiple visits, among others. In some of these problems the regularity is not a property desirable by itself, but it helps to minimise costs. In fact, when the costs are proportional to the square of the distances, the problem of minimising costs and the RTVP are equivalent. The RTVP is very hard to be solved (it has been demonstrated that it is NP-hard). The size of the RTVP instances that can be solved optimally with the best exact method existing in the literature has a practical limit of 40 units. On the other hand, the non-exact methods proposed in the literature to solve larger instances are simple heuristics that obtains solutions quickly, but the quality of the obtained solutions can be improved. Thus, the solution methods existing in the literature are not enough to solve the RTVP. The main objective of this thesis is to improve the resolution of the RTVP. This objective is split in the two following sub-objectives: 1) to increase the size of the RTVP instances that can be solved optimally in a practical computing time; and 2) to obtain efficiently near-optimal solutions for larger instances. Moreover, the thesis has the following two secondary objectives: a) to research the use of metaheuristics under the scheme of hyper-heuristics, and b) to design a systematic, hands-off procedure to set the suitable values of the algorithm parameters. To achieve the aforementioned objectives, several procedures have been developed. To solve the RTVP an exact procedure based on the branch and bound technique has been designed and the size of the instances that can be solved in a practical time has been increased to 55 units. For larger instances, heuristic, heuristic, metaheuristic and hyper-heuristic procedures have been designed, which can obtain optimal or near-optimal solutions quickly. Moreover, a systematic, hands-off fine-tuning method that takes advantage of the two existing ones (Nelder & Mead algorithm and CALIBRA) has been proposed.Award-winningPostprint (published version

Um Algoritmo Híbrido entre Evolução Diferencial e Neder - Mead Usando Entropia para Problemas de Otimização Não - Linear Inteiro Misto.

Vários problemas em engenharia são formulados como problemas de otimização não-lineares inteiros mistos. Métodos estocásticos vem sendo utilizados devido ao seu desempenho, flexibilidade, adaptabilidade e robustez. Evolução Diferencial pode ser utilizado em funções de qualquer natureza e possui habilidades em busca global, porém, tais habilidades não são refletidas na busca local. Este trabalho propõe uma abordagem híbrida entre os algoritmos Evolução Diferencial e Nelder-Mead para problemas de otimização não-linear inteira misto, onde o chaveamento é realizado através da entropia da população. O algoritmo Nelder-Mead foi estendido para manipular variáveis inteiras. O primeiro protótipo foi desenvolvido para solucionar problemas de otimização não-linear inteira sem restrições. O método Alfa Constrained foi incorporado para tratar problemas de otimização não-linear inteira com restrições e o algoritmo demonstrou sua eficácia. Por último, a abordagem foi testada utilizando problemas de otimização não-linear inteira mista com restrições e superou alguns resultados reportados na literatura. A principal vantagem deste método é a habilidade de realizar o chaveamento de acordo com a entropia da população durante a busca

Evolutionary Algorithms and Computational Methods for Derivatives Pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm