934 research outputs found
Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; EspañaFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de IngenierÃa; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Matemática BahÃa Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática BahÃa Blanca; ArgentinaFil: González, Begoña. Universidad de Las Palmas de Gran Canaria; EspañaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de IngenierÃa; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de EconomÃa. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin
Integrating continuous differential evolution with discrete local search for meander line RFID antenna design
The automated design of meander line RFID antennas is a discrete self-avoiding walk(SAW) problem for which efficiency is to be maximized while resonant frequency is to beminimized. This work presents a novel exploration of how discrete local search may beincorporated into a continuous solver such as differential evolution (DE). A prior DE algorithmfor this problem that incorporates an adaptive solution encoding and a bias favoringantennas with low resonant frequency is extended by the addition of the backbite localsearch operator and a variety of schemes for reintroducing modified designs into the DEpopulation. The algorithm is extremely competitive with an existing ACO approach and thetechnique is transferable to other SAW problems and other continuous solvers. The findingsindicate that careful reintegration of discrete local search results into the continuous populationis necessary for effective performance
PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem
Multi-Objective Optimization Problems (MOPs) have attracted growing attention
during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have
been extensively used to address MOPs because are able to approximate a set of
non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment
Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP
which has been extensively studied, and used in several real-life applications.
The mQAP is defined as having as input several flows between the facilities
which generate multiple cost functions that must be optimized simultaneously.
In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm
to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on
an island model that structures the population by creating sub-populations. The
memetic algorithm on each island individually evolve a reduced population of
solutions, and they asynchronously cooperate by sending selected solutions to
the neighboring islands. The experimental results show that our approach
significatively outperforms all the island-based variants of the
multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a
suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary
Computation 2017 (CEC 2017
An adaptation reference-point-based multiobjective evolutionary algorithm
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.It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics
Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
The large number of possible configurations of modern software-based systems,
combined with the large number of possible environmental situations of such
systems, prohibits enumerating all adaptation options at design time and
necessitates planning at run time to dynamically identify an appropriate
configuration for a situation. While numerous planning techniques exist, they
typically assume a detailed state-based model of the system and that the
situations that warrant adaptations are known. Both of these assumptions can be
violated in complex, real-world systems. As a result, adaptation planning must
rely on simple models that capture what can be changed (input parameters) and
observed in the system and environment (output and context parameters). We
therefore propose planning as optimization: the use of optimization strategies
to discover optimal system configurations at runtime for each distinct
situation that is also dynamically identified at runtime. We apply our approach
to CrowdNav, an open-source traffic routing system with the characteristics of
a real-world system. We identify situations via clustering and conduct an
empirical study that compares Bayesian optimization and two types of
evolutionary optimization (NSGA-II and novelty search) in CrowdNav
Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks
Congress on Evolutionary Computation. Singapore, 25-28 September 2007A mobile adhoc network (MANETs) is a self-configuring network of mobile routers (and associated hosts). The routers tend to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change fast and unpredictably. Nowadays, these networks are having a great influence due to the fact that they can create networks without a specific infrastructure. In MANETs message broadcasting is critical to network existence and organization. The broadcasting strategy in MANETs can be optimized by defining a multiobjective problem whose inputs are the broadcasting algorithm's parameters and whose objectives are: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. The network can be simulated to obtain the expected response to a given set of parameters. In this paper, we face this multiobjective problem with two algorithms: Multiobjective Particle Swarm Optimization and ESN (Evolution Strategy with NSGAII). Both algorithms are able to find an accurate approximation to the Pareto optimal front that is the solution of the problem. ESN improves the results of MOPSO in terms of the set coverage and hypervolume metrics used for comparison
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