Abstract:This article is concerned with optimization of very large steel structures subjected to the actual constraints of the American Institute of Steel Construction ASD and LRFD specifications on high-performance multiprocessor machines using biologically inspired genetic algorithms. First, parallel fuzzy genetic algorithms (GAs) are presented for optimization of steel structures using a distributed memory Message Passing Interface (MPI) with two different schemes: the processor farming scheme and the migration sche… Show more
“…Interactive design with reference to steel structure design is achieved by integrating fuzzy logic into the constraint handling in [58,57]. In order to handle large steel structures the algorithm proposed in [59] expands the previous the two studies above in a parallel fashion. This paper proposes a novel progressive preference articulation mechanism for multi-objective optimisation problems.…”
Section: Progressive Preference Articulation: a Brief Reviewmentioning
Abstract.This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA).WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation.Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest.
“…Interactive design with reference to steel structure design is achieved by integrating fuzzy logic into the constraint handling in [58,57]. In order to handle large steel structures the algorithm proposed in [59] expands the previous the two studies above in a parallel fashion. This paper proposes a novel progressive preference articulation mechanism for multi-objective optimisation problems.…”
Section: Progressive Preference Articulation: a Brief Reviewmentioning
Abstract.This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA).WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation.Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest.
“…The algorithm implemented based on MapReduce framework can be expanded to large-scale clusters, without changing any code. This is highly beneficial for the solution to problems of variable scale, especially in the case of processing mass data [10,11,12].…”
Section: Rowing Acceleration Of Lsa-sam Evaluation Model Based On Mapmentioning
confidence: 99%
“…So, we have: (10) It is assumed that in MR-LSA-GCC there is (are) m virtual machine (s) to conduct LSA-GCC algorithm as slave nodes, hence there is Ts=m*Tmap. Accordingly, there is: It can be observed from the formula that the acceleration rate of MR-LSA-GCC depends on the time of conducting LSA-GCC algorithm.…”
Section: Analysis Of Acceleration Rate Based On Mapreduce Frameworkmentioning
“…al., 2001). In this study, only direct cost is considered as a substantial cost and GAs have been used productively in a number of studies in order to optimize costs Adeli 2001, Sarma andAdeli 2002).…”
Section: Optimization Using Genetic Algorithmsmentioning
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