30,701 research outputs found
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
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
A service oriented architecture for engineering design
Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms
(MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers
a potential solution to the compute-intensive nature of this objective function evaluation, by
allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Large-scale optimization problems that involve thousands of decision
variables have extensively arisen from various industrial areas. As a powerful
optimization tool for many real-world applications, evolutionary algorithms
(EAs) fail to solve the emerging large-scale problems both effectively and
efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA
that can not only produce high-quality solution by solving sub-problems
separately, but also highly utilizes the power of parallel computing by solving
the sub-problems simultaneously. Existing DC-based EAs that were deemed to
enjoy the same advantages of the proposed algorithm, are shown to be
practically incompatible with the parallel computing scheme, unless some
trade-offs are made by compromising the solution quality.Comment: 12 pages, 0 figure
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Recently, increasing works have proposed to drive evolutionary algorithms
using machine learning models. Usually, the performance of such model based
evolutionary algorithms is highly dependent on the training qualities of the
adopted models. Since it usually requires a certain amount of data (i.e. the
candidate solutions generated by the algorithms) for model training, the
performance deteriorates rapidly with the increase of the problem scales, due
to the curse of dimensionality. To address this issue, we propose a
multi-objective evolutionary algorithm driven by the generative adversarial
networks (GANs). At each generation of the proposed algorithm, the parent
solutions are first classified into real and fake samples to train the GANs;
then the offspring solutions are sampled by the trained GANs. Thanks to the
powerful generative ability of the GANs, our proposed algorithm is capable of
generating promising offspring solutions in high-dimensional decision space
with limited training data. The proposed algorithm is tested on 10 benchmark
problems with up to 200 decision variables. Experimental results on these test
problems demonstrate the effectiveness of the proposed algorithm
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
Automatic surrogate model type selection during the optimization of expensive black-box problems
The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate model type selection framework that is able to dynamically select the best model type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
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