Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page

Multidiscipinary Optimization For Gas Turbines Design

State-of-the-art aeronautic Low Pressure gas Turbines (LPTs) are already characterized by high quality standards, thus they offer very narrow margins of improvement. Typical design process starts with a Concept Design (CD) phase, defined using mean-line 1D and other low-order tools, and evolves through a Preliminary Design (PD) phase, which allows the geometric definition in details. In this framework, multidisciplinary optimization is the only way to properly handle the complicated peculiarities of the design. The authors present different strategies and algorithms that have been implemented exploiting the PD phase as a real-like design benchmark to illustrate results. The purpose of this work is to describe the optimization techniques, their settings and how to implement them effectively in a multidisciplinary environment. Starting from a basic gradient method and a semi-random second order method, the authors have introduced an Artificial Bee Colony-like optimizer, a multi-objective Genetic Diversity Evolutionary Algorithm [1] and a multi-objective response surface approach based on Artificial Neural Network, parallelizing and customizing them for the gas turbine study. Moreover, speedup and improvement arrangements are embedded in different hybrid strategies with the aim at finding the best solutions for different kind of problems that arise in this field.Comment: 12 pages, 6 figures. Presented at the XXII Italian Association of Aeronautics and Astronautics Conference (2013

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201