Performance evaluation and optimization of swarms of robots in a specific task

Objectives and methodology: Nowadays the swarms of robots represent an alternative to solve a wide range of tasks as search, aggregation, predatorprey, foraging, etc. However, determining how well the task is resolved is an important current problem, assign evaluation metrics to tasks performed by swarms of robots is very useful in order to measure the performance of a particular swarm in the task resolution. Find the control parameters of a swarm of robots that resolves a task with the best possible performance represents many benefits as saving of energetic resources and time. The general objective in this thesis is to evaluate and improve the performance of a swarm of robots in the resolution of a particular task, for that reason the following specific objectives are proposed: 1) To describe a flocking task with target zone search and to determine evaluation metrics that measure the task resolution; 2) To implement behavior policies for a simulated swarm of quadrotors; 3) To implement multi-objective optimization techniques in order to find the best sets of control parameters of the swarm that resolve the proposed task with the best possible performance; 4) To compare the performance of the implemented multi-objective optimization algorithms in order to determine which algorithm represents the best option to optimize this type of tasks. Different methods to control swarms of robots have been proposed, in this thesis a bio-inspired model based in repulsion (∆r), orientation (∆o) and attraction (∆a) tendencies between biological species as bird flocks and schools of fish is applied in the simulated swarm of quadrotors. Different experiments are proposed, the flocking task with target zone search is optimized for swarms of quadrotors of 5, 10 and 20 members and with two different conditions in the environment, one case without obstacles and another case with obstacles in the arena. The task is evaluated by four proposed objective functions formulated as minimization problems which are oriented to reach four main objectives in the task, as these objectives functions are minimized the desired behavior of the swarm of quadrotors is reached. The Multi-Objective Particle Swarm Optimization (MOPSO), the Nondominated Sorting Genetic Algorithm II using Differential Evolution (NSGA-II-DE) and the Multiobjective Evolutionary Algorithm based on Decomposition using Differential Evolution (MOEA/D-DE) are used to optimize the control parameters ∆r, ∆o and ∆a for the proposed task in each experiment. The Hypervolume measure (HV ), a modified C-metric (Q) and the time per cycle (T P C) are the selected metrics to evaluate the performance of the multi-objective optimization algorithms. Contributions and conclusions: The obtained results show that the selected behavior policies produces collaborative interactions between members of the swarm that benefit the resolution of the task. Use multi-objective optimization techniques directly on the quadrotor swarm simulator produces small number of optimized solutions because the optimization process is only suitable with small populations and with a reduced number of cycles due to the..

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

A Powerful Optimization Tool for Analog Integrated Circuits Design

This paper presents a new optimization tool for analog circuit design. Proposed tool is based on the robust version of the differential evolution optimization method. Corners of technology, temperature, voltage and current supplies are taken into account during the optimization. That ensures robust resulting circuits. Those circuits usually do not need any schematic change and are ready for the layout.. The newly developed tool is implemented directly to the Cadence design environment to achieve very short setup time of the optimization task. The design automation procedure was enhanced by optimization watchdog feature. It was created to control optimization progress and moreover to reduce the search space to produce better design in shorter time. The optimization algorithm presented in this paper was successfully tested on several design examples

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Multi-objective engineering shape optimization using differential evolution interfaced to the Nimrod/O tool

This paper presents an enhancement of the Nimrod/O optimization tool by interfacing DEMO, an external multiobjective optimization algorithm. DEMO is a variant of differential evolution – an algorithm that has attained much popularity in the research community, and this work represents the first time that true multiobjective optimizations have been performed with Nimrod/O. A modification to the DEMO code enables multiple objectives to be evaluated concurrently. With Nimrod/O’s support for parallelism, this can reduce the wall-clock time significantly for compute intensive objective function evaluations. We describe the usage and implementation of the interface and present two optimizations. The first is a two objective mathematical function in which the Pareto front is successfully found after only 30 generations. The second test case is the three-objective shape optimization of a rib-reinforced wall bracket using the Finite Element software, Code_Aster. The interfacing of the already successful packages of Nimrod/O and DEMO yields a solution that we believe can benefit a wide community, both industrial and academic

On the detection of nearly optimal solutions in the context of single-objective space mission design problems

When making decisions, having multiple options available for a possible realization of the same project can be advantageous. One way to increase the number of interesting choices is to consider, in addition to the optimal solution x*, also nearly optimal or approximate solutions; these alternative solutions differ from x* and can be in different regions – in the design space – but fulfil certain proximity to its function value f(x*). The scope of this article is the efficient computation and discretization of the set E of e–approximate solutions for scalar optimization problems. To accomplish this task, two strategies to archive and update the data of the search procedure will be suggested and investigated. To make emphasis on data storage efficiency, a way to manage significant and insignificant parameters is also presented. Further on, differential evolution will be used together with the new archivers for the computation of E. Finally, the behaviour of the archiver, as well as the efficiency of the resulting search procedure, will be demonstrated on some academic functions as well as on three models related to space mission design