About Designing an Observer Pattern-Based Architecture for a Multi-objective Metaheuristic Optimization Framework

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Light beam search based multi-objective optimization using evolutionary algorithms

For the past decade or so, evolutionary multi-objective optimization (EMO) methodologies have earned wide popularity for solving complex practical optimization problems, simply due to their ability to find a representative set of Pareto-optimal solutions for mostly two, three, and some extent to four and five-objective optimization problems. Recently, emphasis has been made in addressing the decision-making activities in arriving at a single preferred solution. The multiple criteria decision making (MCDM) literature offers a number of possibilities for such a task involving user preferences which can be supplied in different forms. This paper presents an interactive methodology for finding a preferred set of solutions, instead of the complete Pareto-optimal frontier, by incorporating preference information of the decision maker. Particularly, we borrow the concept of light beam search and combine it with the NSGA-II procedure. The working of this procedure has been demonstrated on a set of test problems and on engineering design problems having two to ten objectives, where the obtained solutions are found to match with the true Pareto-optimal solutions. The results highlight the utility of this approach towards eventually facilitating a better and more reliable optimization-cum-decision-making task

Multi-agent collaborative search : an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher

Modelling of laboratory data of bi-directional reflectance of regolith surface containing Alumina

Bidirectional reflectance of a surface is defined as the ratio of the scattered radiation at the detector to the incident irradiance as a function of geometry. The accurate knowledge of the bidirectional reflection function (BRF) of layers composed of discrete, randomly positioned scattering particles is very essential for many remote sensing, engineering, biophysical applications and in different areas of Astrophysics. The computations of BRF's for plane parallel particulate layers are usually reduced to solve the radiative transfer equation (RTE) by the existing techniques. In this work we present our laboratory data on bidirectional reflectance versus phase angle for two sample sizes of 0.3 and 1 $\mu m$ of Alumina for the He-Ne laser at 632.8 nm (red) and 543.5nm(green) wavelength. The nature of the phase curves of the asteroids depends on the parameters like- particle size, composition, porosity, roughness etc. In our present work we analyse the data which are being generated using single scattering phase function i.e. Mie theory considering particles to be compact sphere. The well known Hapke formula will be considered along with different particle phase function such as Mie and Henyey Greenstein etc to model the laboratory data obtained at the asteroid laboratory of Assam University.Comment: 5 pages, 5 figures [accepted for publication in Publications of the Astronomical Society of Australia (PASA) on 8 June, 2011

Complementarity Problem With Nekrasov ZZ tensor

It is worth knowing that a particular tensor class belongs to $P$-tensor which ensures the compactness to solve tensor complementarity problem (TCP). In this study, we propose a new class of tensor, Nekrasov $Z$ tensor, in the context of the tensor complementarity problem. We show that the class of $P$-tensor contains the class of even ordered Nekrasov $Z$ tensors with positive diagonal elements. In this context, we propose a procedure by which a Nekrasov $Z$ tensor can be transformed into a tensor which is diagonally dominant. Keywords: Diagonally dominant tensor, Nekrasov tensors, Nonsingular $H$ tensor, Nekrasov $Z$ tensor, $P$-tensors, Tensor complementarity proble