In order to achieve thousand-fold throughput improvements for future applications, heterogeneous cellular networks (HCNs) with ultra-densely deployed small cells have gained a great deal of attention ...in recent years. However, energy consumption is an increasing concern as base station deployment (BSs) becomes denser. In order to address this issue, a novel framework is proposed for integrating multiple reconfigurable intelligent surfaces (RISs) in heterogeneous cellular networks (HCNs), where the communication in each BS is enhanced by its associated RIS. The multi-objective optimization is formulated to obtain the optimal trade-off between throughput and energy consumption. Moreover, an improved non-dominated sorting genetic algorithm II (NSGA-II) based algorithm is proposed to optimize the sub-carrier assignment, user equipment (UE) association, power allocation, and RISs’ phase shift. The proposed algorithm’s potential solutions are first encoded as individuals and optimized with the conventional NSGA-II algorithm. In order to further tackle the coupling characters of the optimization variables, the Zoutendijk method is further employed for searching for better solutions. Numerical results illustrate that: (i) the proposed multiple-RIS-assisted HCNs outperform conventional HCNs without RIS. (ii) the performance can be improved by deploying the RIS closer to UEs or increasing the number of reflecting elements.
This paper introduces a novel unsupervised text feature selection technique that combines the multi-objective evolutionary algorithm NSGA II with a local Hill Climbing based search. The objective ...functions in NSGA II are an adapted version of the Mean Absolute Difference criterion and the Holes projection pursuit index, both of which incorporate information regarding the cardinality of the selected feature subset. The local Hill Climbing procedure aims to further improve the solutions provided by NSGA II through elimination/replacement of redundant features. The redundancy level of a feature F in a chromosome is measured via the lexical and semantic similarities between F and the other features represented in the chromosome. Experiments conducted on three real-world textual subdatasets indicate that our proposed approach achieves very good results in comparison to four state-of-the-art evolutionary unsupervised feature selection techniques and to one classical feature extraction method with proven effectiveness (Principal Component Analysis).
High-durability concrete is required in extremely cold or ocean environments, making the design of concrete mixes highly important and complicated. In this study, a hybrid intelligent framework for ...multi-objective optimization based on random forest (RF) and the non-dominated sorting genetic algorithm version II (NSGA-II) is developed to efficiently predict concrete durability and optimize the concrete mix ratio. The relative dynamic elastic modulus of concrete after 300 freeze–thaw cycles and the chloride ion permeability coefficient at 28 days are defined as the standard measures of durability. The concrete mix ratio is taken as the influencing parameter, and orthogonal test data and engineering practice data are collected as the datasets. The proposed framework is applied to a realistic expressway project in a cold region of China. The results demonstrate that (1) a hybrid intelligent framework based on RF-NSGA-II can effectively predict concrete durability and optimize the mix ratio. (2) The developed RF model has an excellent regression learning ability, while the goodness of fit (R2) of concrete durability reaches 0.9503 and 0.9551, respectively, with root mean square error (RMSE) values of only 0.096 and 0.043, the mean absolute percentage error (MAPE) values of 2.54% and 2.17%. (3) After optimization, the concrete durability reaches a high standard, with a frost resistance of >95% and a chloride ion permeability coefficient of <3*10−8 cm2/s, at a unit volume cost of only 376.77 yuan. Hence, the proposed framework can be used to effectively optimize the concrete mix design and provide guidance for similar projects.
•A novel multiobjective methodology for optimization of preventive maintenance programs in electric power distribution systems is proposed.•The mathematical model of dynamic programming comprises the ...philosophy of reliability centered maintenance (RCM).•The reliability index of each equipment is considered as a predictive index.•Fuzzy logic inference system is used to update dynamically the reliability index of equipment in the time horizon.•We propose a Nondominated Sorting Genetic Algorithm to solve the multiobjective optimization problem.
This paper proposes a multiobjective model to solve the mathematical problem of optimizing reliability-centered maintenance planning of an electric power distribution system (EPDS). The main goal is to minimize the preventive maintenance costs while maximizing the index of reliability of the whole system. In the proposed model, the limits of the indices, such as SAIDI and SAIFI, are considered as constraints of the maintenance programs. The reliability indices of the EPDS components are evaluated and updated by a fuzzy inference system. A NSGA-II algorithm was proposed to solve the multiobjective model that provides an optimized Pareto frontier. The results obtained from applying the proposed methodology to a system with three feeders and 733 components are presented, showing its robustness and quality for maintenance planning in EPDS.
This work presents a bi-objective time-dependent vehicle routing problem with delivery failure probabilities (TDVRPDFP). Two objectives are jointly minimized: operational costs and delivery failure ...rates. Both travel times and costs, as well as the probabilities of delivery failure and service times and costs, are considered time-dependent. A mixed-integer linear programming model is proposed to obtain sets of non-dominated solutions for small-size instances, while a multi-objective genetic algorithm, NSGA-II, is implemented to obtain approximate sets of non-dominated for large-size instances. Five sets of instances are proposed and used to evaluate the solution approaches. Our results indicate that the implemented NSGA-II algorithm can optimally solve small and large instances and find large Pareto fronts for instances with more than 25 nodes and 40-time intervals.
•The time-dependent failure rates in customer product deliveries is incorporated to a vehicle routing problem.•A bi-objective MILP for the time-dependent vehicle routing problem with delivery failure probabilities (TDVRPDFP) is proposed.•A bi-objective NSGA-II heuristic for the TDVRPDFP for solving small and large cases of the problem is presented.•A non-deterministic procedure to assess the increase in operating costs associated with failed deliveries is proposed.
A hybrid model with multiple sub-classifier is a powerful classifier for intelligent fault diagnosis of gearboxes. Each sub-classifier is used to deal with different gear or bearing faults, and thus ...each should have its own optimal feature group. However, these sub-classifiers in the hybrid model used the same feature group in most reported feature selection methods. To overcome this problem, this study brings some insights into optimization on sub-classifiers. This proposed method, known as block feature selection (BFS), optimizes the feature group for only one fault type in each sub-classifier. The selected groups are then amalgamated to form the optimal features for all types of gearbox failures, with corresponding labels in the hybrid model. Each sub-classifier is considered to be one block. In each block, the concept of NSGA-II is used to select features for its corresponding sub-classifier. Then, a new sorting algorithm is proposed to find the best feature group of sub-classifier. In general, BFS can optimize features of each sub-classifier to find the best feature group for hybrid model. The effectiveness of BFS is validated by experimental data from bearing and gear faults in a planetary gearbox rig. The robustness of BFS is verified by incorporating white noise at different levels.
The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and ...diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations.
•Combined economic emission dispatch problem is solved by ABC-NSGA-II Algorithm.•Best compromising solution, best fuel cost, and best emission values are compared.•Results of combined economic emission dispatch problem in old articles are analyzed.•Multi-criteria decision-making methods are used to find trade-off solution.•Statistical analysis using hypervolume indicator is presented.
Currently the generative adversarial networks (GANs) have rapidly become a popular research hotspot that people concerned and have been applied to various fields. Lots of meaningful work have been ...proposed and various variants of GANs sprung up in last few years. The scholars usually design GAN structure like the layers and hyperparameters setting according to the experience and constantly attempts. For the propose of finding the appropriate structure more conveniently and efficiently. A method with multiobjective algorithm is proposed to obtain the optimal structure for the GANs. In the proposed method, the nondominated sorting genetic algorithm II (NSGA‐II) is utilized to optimize the hyperparameters and structure of deep convolutional generative adversarial network (DCGAN). The experiments are conducted on MNIST and Malware datasets demonstrate the efficiency and high performance of proposed method.
•A viable method is proposed for spatial design of water quality monitoring network.•The proposed method was applied in surface water and a road construction project.•The integrated form of ...transinformation entropy and value of information was used.•The application of NSGA-II and III based optimization model were assessed.•The TOPSIS, PROMETHEE, and AHP models were utilized for decision-making.
The environmental impacts of road construction on the aquatic environment necessitate the monitoring of receiving water quality. The main contribution of the paper is developing a feasible methodology for spatial optimization of the water quality monitoring network (WQMN) in surface water during road construction using the field data. First, using the Canadian Council of Ministers of the Environment (CCME) method, the water quality index (WQI) was computed in each potential monitoring station during construction. Then, the integrated form of the information-theoretic techniques consists of the transinformation entropy (TE), and the value of information (VOI) were calculated for the potential stations. To achieve the optimal WQMNs, the Non-dominated Sorting Genetic Algorithm II and III (NSGA-II, and III) based multi-objective optimization models were developed considering three objective functions, including i) minimizing the number of stations, ii) maximizing the VOI in the selected network, and iii) minimizing redundant information for the selected nodes. Finally, three multi-criteria decision-making models, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), and Analytical Hierarchy Process (AHP) were utilized for choosing the best alternative among Pareto optimal solutions considering various weighing scenarios assigned to criteria. The applicability of the presented methodology was assessed in a 22 km long road construction site in southern Norway. The results deliver significant knowledge for decision-makers on establishing a robust WQMN in surface water during road construction projects.
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