Agencies who provide social care services typically have to optimise staff allocations and the travel whilst attempting to satisfy conflicting objectives. In such cases it is desirable to have a range of solutions to ...
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ISBN:
(纸本)9781450349208
Agencies who provide social care services typically have to optimise staff allocations and the travel whilst attempting to satisfy conflicting objectives. In such cases it is desirable to have a range of solutions to choose from, allowing the agency's planning staff to explore the various options available This paper examines the use of multi-objective evolutionary algorithms to produce solutions to the Workforce Scheduling and Routing Problem (WSRP) formulated with three objectives which should be minimised" financial cost, CO2 emissions and car use. We show that financial cost and CO2 increase with the size of the problem and the imposed constraints. In order to support the planning staff in their decision making, we present an evolutionary Algorithm based support tool that will identify a group of solutions from the Pareto front which match criteria specified by the planner. We demonstrate that our approach is able to find a wide range of solutions, which enhance the flexibility of the agencys choices, the decision support tool subsequently allows the planner to discover small groups of solutions that meet their specific requirements.
This article describes using an evolutionary algorithms to finding an original rule of generated 1D cellular automaton. In this article will be verified possibility and precision of searching rule from generated 1D ce...
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ISBN:
(纸本)9783319484990;9783319484983
This article describes using an evolutionary algorithms to finding an original rule of generated 1D cellular automaton. In this article will be verified possibility and precision of searching rule from generated 1D cellular automaton using different evolutionary algorithms, random generators and rule definition. This extractor can be used for finding cellular automaton rule generating animals patterns like pigment patterns on the shells of mollusks.
Business processes are a collection of related, structured activities performed to achieve a defined business outcome. Adopting a business process perspective is an essential advantage for organizations to orchestrate...
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ISBN:
(纸本)9781538630341
Business processes are a collection of related, structured activities performed to achieve a defined business outcome. Adopting a business process perspective is an essential advantage for organizations to orchestrate and achieve continuous improvements on time and within specified resource constraints. The increased popularity of this domain, however, has resulted in a variety of interdisciplinary approaches with limited tangible, quantifiable - and thus measurable-benefits. Operational Research (OR) has critically evolved during the last decades, providing businesses and organizations with problem-solving techniques and methods aiming to enhanced performance and improved efficiency. The proposed project focuses on the development, evaluation and verification of a business process optimisation framework as the central objective of the PhD Thesis. The performed optimisation is intended to use evolutionary Computing (EC) techniques, as they have been used effectively in a variety of similar problems. The author seeks advice and feedback on the optimal theoretical foundation of the framework, the utilization methods adopted (i.e. in the area of continuous and discrete computational optimization) and the method selection for performance analysis and validation. Furthermore, guidance from experts on the field will decisively influence the PhD Thesis, through directing its orientation to current research trends and future opportunities.
In order to effectively design nearly Zero Energy Buildings, the assessment of energy performance in the early design stages through simulation is an important, although very demanding and complex, procedure. Over the...
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In order to effectively design nearly Zero Energy Buildings, the assessment of energy performance in the early design stages through simulation is an important, although very demanding and complex, procedure. Over the last decades, various tools and methods have been developed to address performance-related design questions, mostly using Multi-Objective Optimization algorithms. Technological advances have revolutionized the way Architects design and think, automating complex tasks and allowing the assessment of multiple variants at the same time. In this paper, a new nZEB design workflow methodology is proposed, integrating evolutionary algorithms and energy simulation, and its capabilities and current limitations are explored. (C) 2017 The Authors. Published by Elsevier Ltd.
In this paper, we study the effect of Meta-Lamarckian learning on the performance of a generic hybrid Multi-objective evolutionary Algorithm based on Decomposition (MOEA/D) to solve a well-known combinatorial Multi-Ob...
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ISBN:
(纸本)9781538637319
In this paper, we study the effect of Meta-Lamarckian learning on the performance of a generic hybrid Multi-objective evolutionary Algorithm based on Decomposition (MOEA/D) to solve a well-known combinatorial Multi-Objective Optimization (MOO) problem. We study the hybridization of MOEA/D with a pool of six general-purpose heuristics so as to locally optimize the solutions during the evolution. We initially consider the six individualistic hybrid MOEA/D's, in which at every step of the evolution the same local search heuristic from the generic pool is applied. MOEA/D is then enriched with a learning strategy that, based on the problem's properties and objective functions, adaptively selects at each step of the evolution and for each problem neighbourhood the best performing local search heuristic from the generic pool of heuristics. The proposed method is evaluated on various test instances of a multi-objective Permutation Flow Shop Scheduling Problem (MO-PFFSP): given a set of jobs and a series of machines, the corresponding processing time of each job on every machine and the due dates of each job, determine a processing order of the jobs on each machine, so as to simultaneously minimize the makespan (total completion time), and the maximum job tardiness. The results of our experimental studies suggest that the proposed method successfully learns the behaviour of individual local search heuristics during the evolution outperforming in terms of both convergence and diversity the conventional MOEA/D and the individualistic hybrid MOEA/D's. The proposed method does not utilize any problem-specific heuristics, and as a result, is readily applicable to other combinatorial MOO problems.
This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since...
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This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer's revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution quality, run-time complexity and trade-off between convergence and diversity, and on the other hand, it's significant superiority to well-known heuristic and exact WD techniques that have been implemented for much simpler CRAs.
Log-periodic antenna is a special antenna type utilized with great success in many broadband applications due to its ability to achieve nearly constant gain over a wide frequency range. Such antennas are extensively u...
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ISBN:
(纸本)9789082598704
Log-periodic antenna is a special antenna type utilized with great success in many broadband applications due to its ability to achieve nearly constant gain over a wide frequency range. Such antennas are extensively used in electromagnetic compatibility measurements, spectrum monitoring and TV reception. In this study, a log-periodic dipole array is measured, simulated, and then optimized in the 470-860 MHz frequency band. Two simulations of the antenna are initially performed in time and frequency domain respectively. The comparison between these simulations is presented to ensure accurate modelling of the antenna. The practically measured net gain is in good agreement with the simulated net gain. The antenna is then optimized to concurrently improve voltage standing wave ration, net gain and front-to-back ratio. The optimization process has been implemented by using various algorithms included in CST Microwave Studio, such as Trusted Region Framework, Nelder Mead Simplex algorithm, Classic Powell and Covariance Matrix Adaptation evolutionary Strategy (CMA-ES). The Trusted Region Framework seems to have the best performance in sufficiently optimizing all predefined goals specified for the antenna.
The general recommendation for the mutation rate in standard-bit mutation is 1/n, which gives asymptotically optimal expected optimization times for several simple test problems. Recently, Doerr et al. have shown that...
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ISBN:
(纸本)9781450349390
The general recommendation for the mutation rate in standard-bit mutation is 1/n, which gives asymptotically optimal expected optimization times for several simple test problems. Recently, Doerr et al. have shown that such mutation rate is not ideal, and is far from optimal for multimodal problems. They proposed the heavy-tailed mutation operator fmut(beta) which significantly improves performance of the (1+1) evolutionary algorithm on Jump problem and yields similar speed-ups for the vertex cover problem in bipartite graphs and the matching problem in general graphs. We evaluate the fmut(beta) mutation operator on the problem of hard test generation for the maximum flow algorithms. Experiments show that the fmut(beta) mutation operator greatly increases performance of the (1+1) evolutionary algorithm. It also achieves performance improvement, although less drastic, on a simple population based algorithm, but hinders performance of a crossover based genetic algorithm.
Differential Evolution (DE) is a population-based algorithm which has been successfully used to solve optimization problems. DE algorithm begins with an initial population with some randomly generated candidate soluti...
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ISBN:
(纸本)9781538627266
Differential Evolution (DE) is a population-based algorithm which has been successfully used to solve optimization problems. DE algorithm begins with an initial population with some randomly generated candidate solutions. During evolutionary process, the population of candidate solutions is evolved toward the promising region by using the specific operations. The population in the DE algorithm can resemble an especial perspective of a small society which has individuals to seek a common goal. In a society, the election system is commonly used as an effective approach;which is employed to determine one or several representatives who are responsible to make major decisions. Some machine learning algorithms are inspired from the society election system to develop an enhanced algorithm from a pool of potential algorithms with the complementary performances. This study is motivated from the election systems of societies which can be applied on population-based algorithms, here DE algorithm as a case study. We propose an election-based discrete DE algorithm which uses the information of all candidate solutions to create a new trial solution as a president candidate solution. During optimization phases, after applying the evolutionary operators, all candidate solutions vote to select the values of president's variables. In the proposed method, a majority voting method is applied to choose a value for each variable of the president candidate solution. We employ the discrete DE (DDE) algorithm as the parent algorithm to develop election-based discrete DE (EDDE) which is evaluated on the fifteen discrete benchmark functions. Simulation results confirm that EDDE obtains a promising performance on the majority of these functions.
This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to ...
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