multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most ...
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multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most of these methods adopt arbitrary regularization values for iterative thresholding-based local search, which hardly produces high-precision solutions stably. In this article, we propose a multi-objective sparse reconstruction scheme with novel techniques of transfer learning and localized regularization. Firstly, we design a knowledge transfer operator to reuse the search experience from previously solved homogeneous or heterogeneous sparse reconstruction problems, which can significantly accelerate the convergence and improve the reconstruction quality. Secondly, we develop a localized regularization strategy for iterative thresholding-based local search, which uses systematically designed independent regularization values according to decomposed subproblems. The strategy can lead to improved reconstruction accuracy. Therefore, our proposed scheme is more computationally efficient and accurate, compared to existing multi-objective sparse reconstruction methods. This is validated by extensive experiments on simulated signals and benchmark problems.
It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence str...
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It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions;(2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively;and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.
Despite half-century's unremitting efforts, the prediction of protein structure from its amino acid sequence remains a grand challenge in computational biology and bioinformatics. Two key factors are crucial to so...
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Despite half-century's unremitting efforts, the prediction of protein structure from its amino acid sequence remains a grand challenge in computational biology and bioinformatics. Two key factors are crucial to solving the protein structure prediction (PSP) problem: an effective energy function and an efficient conformation search strategy. In this study, we model the PSP as a multi-objective optimization problem. A three-objective evolution algorithm called AIMOES is proposed. AIMOES adopts three physical energy terms: bond energy, non-bond energy, and solvent accessible surface area. In AIMOES, an evolution scheme which flexibly reuse past search experiences is incorporated to enhance the efficiency of conformation search. A decision maker based on the hierarchical clustering is carried out to select representative solutions. A set of benchmark proteins with 30-91 residues is tested to verify the performance of the proposed method. Experimental results show the effectiveness of AIMOES in terms of the root mean square deviation (RMSD) metric, the distribution diversity of the obtained Pareto front and the success rate of mutation operators. The superiority of AIMOES is demonstrated by the performance comparison with other five state-of-the-art PSP methods. (C) 2018 Elsevier B.V. All rights reserved.
Computational modeling of neuronal morphologies is significant for understanding structure-function relationships and brain information processing in computational neuroscience. Using a gene regulatory network model, ...
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Computational modeling of neuronal morphologies is significant for understanding structure-function relationships and brain information processing in computational neuroscience. Using a gene regulatory network model, an evolutionary developmental approach is presented for efficient generation of 3D virtual neurons. This approach describes the developmental process of dendritic morphologies by locally inter-correlating morphological variables which can be represented by the dynamics of gene expression. Then, the multi-objective evolutionary algorithm with gene segmental duplication and divergence operators is applied to evolve the virtual neurons, which aims at generating virtual neurons that are as good as the experimentally traced real neurons in terms of statistical morphological measurements. We experimentally generated motoneurons and statistically compared between the real neurons and the generated virtual neurons by measuring a series of emergent morphological features. The results show that the generated virtual neurons are seemingly realistic, accurate, and further suggest that this approach is an efficient tool for understanding neural development and investigating the relation of neuronal structure to function in particular. (C) 2017 Elsevier B.V. All rights reserved.
In an actual remanufacturing process, a human operator is able to continuously learn the disassembly knowledge of an end-of-life product when he/she disassembles it, which makes him/her disassemble it more proficientl...
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ISBN:
(纸本)9781728100845
In an actual remanufacturing process, a human operator is able to continuously learn the disassembly knowledge of an end-of-life product when he/she disassembles it, which makes him/her disassemble it more proficiently. In order to describe this feature, this work proposes a stochastic dual objective disassembly sequencing planning problem considering human learning effects. In this problem, actual disassembly and setup time of operations are a function of their normal time and starting time. A new mathematical model is established to maximize total disassembly profit and minimize disassembly time. In order to solve this problem efficiently, a multi-population multi-objective evolutionary algorithm is developed. In this algorithm, some special strategies, e.g., solution representation, crossover operator and mutation operator, are newly designed based on this problem's characteristics. Its effectiveness is well illustrated through several numerical cases and by comparing it with two prior approaches, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. Experimental results demonstrate that the proposed algorithm performs well in solving this problem.
In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed i...
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In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.
Many multi-objective evolutionary algorithms (MOEAs) have been developed for many-objective optimization. This paper proposes a new enhanced oee integral dominance and density selection based evolutionaryalgorithm (c...
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Many multi-objective evolutionary algorithms (MOEAs) have been developed for many-objective optimization. This paper proposes a new enhanced oee integral dominance and density selection based evolutionaryalgorithm (called oee integral-EDEA) for many-objective optimization problems. We firstly construct an m-dimension hyper-plane using the extreme point on the each dimension. Then we replace the distance between the origin point and projection of solution on the reference line of oee integral dominance which recently is proposed in oee integral dominance based evolutionaryalgorithm (oee integral-DEA), with the perpendicular distance between each solution and the hyper-plane to develop an enhanced oee integral dominance. Finally, in order to maintain better diversity, oee integral-EDEA employs density based selection mechanism to select the solution for the next population in the environment selection phase. oee integral-EDEA still inherits clustering operator and ranking operator of oee integral-DEA to balance diversity and convergence. The performance of oee integral-EDEA is validated and compared with five state-of-the-art algorithms on two well-known many-objective benchmark problems with three to fifteen objectives. The results show that oee integral-EDEA is capable of obtaining a solution set with better convergence and diversity.
Purpose - One of the main components of multi-objective, and therefore, many-objectiveevolutionaryalgorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has...
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Purpose - One of the main components of multi-objective, and therefore, many-objectiveevolutionaryalgorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach - Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings - The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III and a modified version of the Non-dominated Sorting Genetic algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value - The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.
Broadband Internet access is central to the regeneration of remote communities and reducing the digital divide between rural and urban regions. This paper focuses on rural communities with limited financial resources,...
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ISBN:
(纸本)9781728121383
Broadband Internet access is central to the regeneration of remote communities and reducing the digital divide between rural and urban regions. This paper focuses on rural communities with limited financial resources, environmental issues including long reach from conurbations, and mountainous or otherwise adverse terrain, typically with limited access to a wired power supply. As such, regular access technologies based on cable or fibre optics are not financially viable. To overcome this challenge, we consider the deployment of a Free-Space Optical (FSO) based relay network as primary technology, using diversity to provide resilience to atmospheric effects. More precisely, this paper describes a novel network planning tool based on a multi-objective evolutionary algorithm (MOEA), that determines the suitable location of FSO relay nodes, taking into account end-to-end link speed and the degree of path diversity. This MOEA approach can account for Line-of-Sight occlusions and allows various compromises to be selected from a Pareto front to suit individual needs. We provide suitable results to show the satisfactory operation of the tool and outline avenues for future development.
Nearly zero-energy buildings (NZEBs) are high energy performance buildings in which part of the amount of energy that these buildings require comes mostly from renewable sources. In order to obtain the target of nearl...
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ISBN:
(纸本)9781728111568
Nearly zero-energy buildings (NZEBs) are high energy performance buildings in which part of the amount of energy that these buildings require comes mostly from renewable sources. In order to obtain the target of nearly zero energy consumption, the electrical loads and the energy sources related to the NZEB building can be organized as a Microgrid, which needs to be optimally sized in his components. The optimal sizing of a Microgrid for NZEBs can be formulated as multi-objective problem. In fact, for the NZEB owner exists an economic target aimed at maximizing profits from micro generation, a different one aimed solely to minimize the energy bought from the market and an environmental target aimed at minimizing the global CO2 emissions. These objectives can enter into conflict and create the need for combined optimization. In this paper, this optimization problem is investigated with an integrated framework addressing the multi-objective optimization and multi-criteria evaluation issues simultaneously Minimize the investment cost, maximize the fraction of energy self-consumed with renewable energy sources and reduce the CO2 emissions will be considered as three objectives for multi-objective optimization. The proposed methodology is applied to a microgrid for a NZEB public building. The simulation results show the effectiveness of the proposed methodology.
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