In many industrial real-world environments, scheduling necessitates continual reactive adjustments due to unpredictable perturbations, leading to the dynamic transformation of predefined static schedules. In this pape...
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In many industrial real-world environments, scheduling necessitates continual reactive adjustments due to unpredictable perturbations, leading to the dynamic transformation of predefined static schedules. In this paper, we introduce a new framework named a two-level evolutionary algorithm (2LEA) as a comprehensive approach for addressing the dynamic flexible job shop scheduling problem. The 2LEA is based on a bi-level optimization design, where the upper level is dedicated to solving the general flexible job shop scheduling problem, and the lower level is used as a new evolutionary operator guided by a probability rate in the upper level, focusing on the optimization of operation sequences. This framework is capable of handling four dynamic events job insertion, job cancellation, machine breakdown, and job replacement using a predictive-reactive rescheduling strategy. By addressing the previously unexplored dynamic event of job replacement, this paper fills a significant gap in the literature and opens avenues for further research. Extensive computational experiments conducted on well-known benchmark instances from the Brandimarte and Hurink datasets demonstrate the effectiveness and efficiency of our proposed scheduling algorithm. Our results showcase the superior performance of 2LEA over state-of-the-art approaches in terms of solution quality, affirming its potential as a leading solution for both static and dynamic scheduling challenges.
In this paper we analyze the links between the agent's reservation utility, bargaining power, and risk aversion in terms of their simultaneous effects on the structure of optimal static contracts. We compare the f...
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In this paper we analyze the links between the agent's reservation utility, bargaining power, and risk aversion in terms of their simultaneous effects on the structure of optimal static contracts. We compare the following principal-agent models in the symmetric and asymmetric information environments: the standard approach, which includes a participation constraint, and a multi-objective (MO) optimization approach in which the objective function is a convex combination of the expected utilities of the principal and the agent. The MO model does not include a participation constraint, but it includes a parameter for the agent's bargaining power. We also study an evolutionary Algorithm implementation of the static principal-agent model to support and extend our analytical results. We show that the numerical solution approximated by our implementation of an evolutionary algorithm is in line with the analytical solutions mentioned before. That is, for every admissible value of the agent's reservation utility, there is a corresponding admissible value of the agent's bargaining parameter, both in the MO approach and the EA implementation.
Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it...
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Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it, generating a chain reaction throughout the entire project. Therefore, it is necessary to select a suitable change plan involving only fewer crucial parts in order to enhance the product’s performance, minimize change cost, and reduce change duration/time. Focusing on the case where the correlation strength between parts cannot be accurately obtained, in this paper we study an interval multi-objective evolutionary algorithm for finding excellent design change plans. Firstly, on the basis of the established multi-layer product network with interval correlation weights, an interval multi-objective optimization model of the product design change planning problem is established, where three new objective functions regarding product performance, carbon trading cost and supply risk are defined. Then, a constraint multi-objective evolutionary algorithm based on interval Pareto dominance is proposed to search for optimal change plans. Several novel operators, including the problem characteristic-guided population update strategy, the probability-based interval Pareto dominance, and the interval constraint handling strategy, are developed to enhance the algorithm’s performance. Finally, the proposed algorithm is compared with eight existing algorithms on the two design change cases, experimental results revealed its effectiveness. IEEE
Surrogate-assisted evolutionary algorithm (SAEA) prevails in the optimization of computationally expensive problems. However, existing SAEAs confront low efficiency in the resolution of high-dimensional problems chara...
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Surrogate-assisted evolutionary algorithm (SAEA) prevails in the optimization of computationally expensive problems. However, existing SAEAs confront low efficiency in the resolution of high-dimensional problems characterized by multiple local optima and multivariate coupling. To this end, this paper offers a dual-drive collaboration surrogate-assisted evolutionary algorithm (DDCSAEA) by coupling feature reduction and reconstruction, which coordinates two unsupervised feature learning techniques, i.e., principal component analysis and autoencoder, in tandem. DDCSAEA creates a low-dimensional solution space by downscaling the target high-dimensional space via principal component analysis and collects promising candidates in the reduced space by collaborating a surrogate-assisted evolutionary sampling with differential mutation. An autoencoder is used to perform the feature reconstruction on the collected candidates for infill-sampling in the target high-dimensional space to sequentially refine the neighborhood landscapes of the optimal solution. Experimental results reveal that DDCSAEA has stronger convergence performance and optimization efficiency against eight state-of-the-art SAEAs on high-dimensional benchmark problems within 200 dimensions.
The Pareto dominance-based evolutionary algorithms can effectively address multiobjective optimization problems (MOPs). However, when dealing with many-objective optimization problems with more than three objectives (...
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The Pareto dominance-based evolutionary algorithms can effectively address multiobjective optimization problems (MOPs). However, when dealing with many-objective optimization problems with more than three objectives (MaOPs), the Pareto dominance relationships cannot effectively distinguish the nondominated solutions in high-dimensional spaces. With the increase of the number of objectives, the proportion of dominance-resistant solutions (DRSs) in the population rapidly increases, which leads to insufficient selection pressure. In this paper, to address the challenges on MaOPs, a knee point-driven many-objective evolutionary algorithm with adaptive switching mechanism (KPEA) is proposed. In KPEA, the knee points determined by an adaptive strategy are introduced for not only mating selection but also environmental selection, which increases the probability of generating excellent offspring. In addition, to remove dominance-resistant solutions (DRSs) in the population, an interquartile range method is adopted, which enhances the selection pressure. Moreover, a novel adaptive switching mechanism between angle-based selection and penalty for selecting solutions is proposed, which is aimed at achieving a balance between convergence and diversity. To validate the performance of KPEA, it is compared with five state-of-the-art many-objective evolutionary algorithms. All algorithms are evaluated on 20 benchmark problems, i.e., WFG1-9, MaF1, and MaF4-13 with 3, 5, 8, and 10 objectives. The experimental results demonstrate that KPEA outperforms the compared algorithms in terms of HV and IGD in most of the test instances.
As global demands for enhanced energy efficiency and environmental protection continue to grow, optimizing the combustion processes of industrial boilers has become a critical challenge. These processes involve comple...
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As global demands for enhanced energy efficiency and environmental protection continue to grow, optimizing the combustion processes of industrial boilers has become a critical challenge. These processes involve complex thermodynamic and chemical reactions that directly impact energy utilization and pollutant emissions. However, existing methods for combustion prediction and optimization often fall short in terms of accuracy and efficiency, making it difficult to adapt to varying operational conditions. This study proposes a thermodynamics-based multi-objective optimization method for industrial boiler combustion. A high-precision combustion prediction model is established using deep learning, and an evolutionary algorithm is employed for multi-objective optimization, aiming to achieve an optimal balance between combustion efficiency and pollutant emissions. The findings of this research not only offer new insights into combustion optimization for industrial boilers but also contribute valuable theoretical and practical implications for enhancing energy utilization efficiency and reducing environmental pollution in industrial production.
This paper aims to showcase the potential application of the metaheuristic approach Quadratic Approximation based Jaya (JaQA) in addressing a single-objective uncertain Fixed Charge Transportation Problem with Damagea...
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This paper aims to showcase the potential application of the metaheuristic approach Quadratic Approximation based Jaya (JaQA) in addressing a single-objective uncertain Fixed Charge Transportation Problem with Damageable items (FCTPD). The incorporation of uncertainties and damage rates for items adds realism to the selected problem model. Subsequently, a comprehensive examination of the FCTPD model is conducted, considering both expected value and chance-constrained cases based on uncertainty theory. Equivalent deterministic formulations are considered for these scenarios. The performance of JaQA is comprehensively benchmarked on a set of ten popular benchmarks of single-objective fixed charge transportation problems (FCTP) from the OR library. In addition to this, it is also evaluated on the deterministic forms of the expected value and chance-constrained cases, and the computational results are compared with other widely recognized approaches such as Jaya algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for five numerical instances A thorough investigation of statistical, convergence, and sensitivity analysis are presented to shed light on the efficacy and stability levels of JaQA over its counterparts. The simulation outcomes and exhaustive statistical analysis show that for all the problems considered, JaQA performed better out of GA, PSO, and Jaya. More specifically, it indicated that the utilization of JaQA provided significant profit maximization of the transportation system, thereby ensuring a promising value of the optimal solution.
Remarkable advancements have been achieved in machine learning and computer vision through the utilization of deep neural networks. Among the most advantageous of these networks is the convolutional neural network (CN...
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Remarkable advancements have been achieved in machine learning and computer vision through the utilization of deep neural networks. Among the most advantageous of these networks is the convolutional neural network (CNN). It has been used in pattern recognition, medical diagnosis, and signal processing, among other things. Actually, for these networks, the challenge of choosing hyperparameters is of utmost importance. The reason behind this is that as the number of layers rises, the search space grows exponentially. In addition, all known classical and evolutionary pruning algorithms require a trained or built architecture as input. During the design phase, none of them consider the process of pruning. In order to assess the effectiveness and efficiency of any architecture created, pruning of channels must be carried out before transmitting the dataset and computing classification errors. For instance, following pruning, an architecture of medium quality in terms of classification may transform into an architecture that is both highly light and accurate, and vice versa. There exist countless potential scenarios that could occur, which prompted us to develop a bi-level optimization approach for the entire process. The upper level involves generating the architecture while the lower level optimizes channel pruning. evolutionary algorithms (EAs) have proven effective in bi-level optimization, leading us to adopt the co-evolutionary migration-based algorithm as a search engine for our bi-level architectural optimization problem in this research. Our proposed method, CNN-D-P (bi-level CNN design and pruning), was tested on the widely used image classification benchmark datasets, CIFAR-10, CIFAR-100 and ImageNet. Our suggested technique is validated by means of a set of comparison tests with regard to relevant state-of-the-art architectures.
Feature selection (FS) is an important data pre-processing technique in classification. It aims to remove redundant and irrelevant features from the data, which reduces the dimensionality of data and improves the perf...
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When a constraint violation occurs due to adding a new load to distribution systems, it can be resolved by reconfiguring the distribution systems by changing the states of switches and/or by minimum necessary investme...
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