Dual-objective Optimization Problem Research Articles

Experimental Research and Improved Neural Network Optimization Based on the Ocean Thermal Energy Conversion Experimental Platform

With the progress of research on ocean thermal energy conversion, the stabI have checked and revised all. le operation of ocean thermal energy conversion experiments has become a problem that cannot be ignored. The control foundation for stable operation is the accurate prediction of operational performance. In order to achieve accurate prediction and optimization of the performance of the ocean thermal energy conversion experimental platform, this article analyzes the experimental parameters of the turbine based on the basic experimental data obtained from the 50 kW OTEC experimental platform. Through the selection and training of experimental data, a GA-BP-OTE (GBO) model that can automatically select the number of hidden layer nodes was established using seven input parameters. Bayesian optimization was used to complete the optimization of hyperparameters, greatly reducing the training time of the surrogate model. Analyzing the prediction results of the GBO model, it is concluded that the GBO model has better prediction accuracy and has a very low prediction error in the prediction of small temperature changes in ocean thermal energy, proving the progressiveness of the model proposed in this article. The dual-objective optimization problem of turbine grid-connected power and isentropic efficiency is solved. The results show that the change in isentropic efficiency of the permeable device is affected by the combined influence of the seven parameters selected in this study, with the mass flow rate of the working fluid having the greatest impact. The MAPE of the GBO model turbine grid-connected power is 0.24547%, the MAPE of the turbine isentropic efficiency is 0.04%, and the MAPE of the turbine speed is 0.33%. The Pareto-optimal solution for the turbine grid-connected power is 40.1792 kW, with an isentropic efficiency of 0.837439.

Adaptive Color Transfer From Images to Terrain Visualizations.

Terrain mapping is not only dedicated to communicating how high or steep a landscape is but can also help to indicate how we feel about a place. However, crafting effective and expressive elevation colors is challenging for both nonexperts and experts. In this article, we present a two-step image-to-terrain color transfer method that can transfer color from arbitrary images to diverse terrain models. First, we present a new image color organization method that organizes discrete, irregular image colors into a continuous, regular color grid that facilitates a series of color operations, such as local and global searching, categorical color selection and sequential color interpolation. Second, we quantify a series of cartographic concerns about elevation color crafting, such as the "lower, higher" principle, color conventions, and aerial perspectives. We also define color similarity between images and terrain visualizations with aesthetic quality. We then mathematically formulate image-to-terrain color transfer as a dual-objective optimization problem and offer a heuristic searching method to solve the problem. Finally, we compare elevation colors from our method with a standard color scheme and a representative color scale generation tool based on four test terrains. The evaluations show that the elevation colors from the proposed method are most effective and that our results are visually favorable. We also showcase that our method can transfer emotion from images to terrain visualizations.

DoFA: Adversarial examples detection for SAR images by dual-objective feature attribution

Synthetic aperture radar (SAR) classification models based on convolutional neural networks have high accuracy, but the models’ security is still threatened by adversarial examples. The high threat of adversarial examples derives from the invisible noise that can cause feature changes within the model. Among many adversarial examples detection methods, feature attribution that is sensitive to feature changes performs well in feature analysis. Unfortunately, the existing feature attribution-based detection methods cannot balance the computational efficiency and detection performance well due to the size and speckle noise of the images in SAR adversarial examples detection. In this work, we propose the Dual-objective Feature Attribution (DoFA) method by using the feature attribution scan block to find the suitable scan granularity. The DoFA method formulates the SAR adversarial examples detection issue as a dual-objective optimization problem and takes the number of subsamples generated by feature analysis and the area under curve (AUC) value of the logistic regression model as the objective functions while the feature scan block’s size, stride, padding, and the number of selected model layers are the decision variables. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to search for the scan block parameters with Pareto optimality so that the DoFA method can automatically obtain the best feature analysis granularity in different scenarios. The experimental results on the FUSAR-Ship dataset have shown that the proposed DoFA method has a higher AUC value under five adversarial attacks and a smaller number of subsamples than the existing adversarial examples detection method.

An edge server deployment method based on optimal benefit and genetic algorithm

With the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.

Research on Trajectory Tracking Control of Driverless Electric Formula Racing Car Based on Game Theory

A game theory based trajectory tracking control method is studied for the dual-objective optimization problem of trajectory tracking the accuracy and driving stability of driverless electric formula racing cars in high-speed trajectory conditions. The general control strategy and the model predictive controller based on the evolutionary game between the two players are designed to optimize their own decisions to achieve the optimal payoff for themselves, and to obtain the optimal solution to the dual-objective optimization problem, by taking the dual objectives of trajectory tracking accuracy and driving stability as the two players in the game. Considering the influence of the dynamic environment, the time-varying interactive game mechanism between two plays is introduced, the game payoff matrix is established, the weights of each subject are determined, and a dynamic replication system is constructed by weight evolution to find the optimal equilibrium strategy for the model prediction controller. The simulated results show that the designed controller can play a significant role in optimizing the trajectory tracking accuracy and driving stability compared to a single model predictive controller under different speed tracking conditions.

Efficiency-Aware Dynamic Service Pricing Strategy for Geo-Distributed Fog Computing

Fog computing provides new ideas for solving big data problems in the Internet of Things (IoT) era. With fog, we can save much time on long-distance transmission, thereby increasing the efficiency of network service. However, to transform fog computing from a technology to a service, we first need to properly handle the trading relationship between users and service provider in fog. In this paper, we design an efficiency-aware dynamic service pricing strategy to optimize the payoffs of both users and provider in fog computing. In the modeling, we design a Stackelberg competition-based model while regarding provider as the market leader, and each individual user as a follower. Users in this model care about how to obtain the most cost-effective services. And provider pays attention to taking full advantage of the geo-distributed characteristic in setting prices with multiple customers. In the performance evaluation part, we carry out experiments using real-world datasets to simulate this continuous negotiation process between supply and demand in fog pricing. The results show that our strategy can solve the dual-objective optimization problem while establishing a stable trading relationship between the two sides.

Optimization of Shore Power Deployment in Green Ports Considering Government Subsidies

Shipping trade and port operations are two of the primary sources of greenhouse gas emissions. The emission of air pollutants brings severe problems to the marine environment and coastal residents’ lives. Shore power technology is an efficient CO2 emission reduction program, but it faces sizeable initial investment and high electricity prices. For shipping companies, energy such as low-sulfur fuels and liquefied natural gas has become an essential supplementary means to meet emission reduction requirements. This research considers the impact of government subsidies on port shore power construction and ship shore power use. It constructs a multi-period dual-objective port shore power deployment optimization model based on minimizing operating costs and minimizing CO2 emissions. Multi-combination subsidy strategies, including unit subsidy rate and subsidy demarcation line, are quantitatively described and measured. The proposed Epsilon constraint method is used to transform and model the dual-objective optimization problem. Numerical experiments verify the effectiveness of the model and the feasibility of the solution method. By carrying out a “cost-environment” Pareto trade-off analysis, a model multi-period change analysis, and a subsidy efficiency analysis, this research compares the decision-making results of port shore power construction, ship berthing shore power use, and ship berthing energy selection. Government subsidy strategy and operation management enlightenment in the optimization of port shore power deployment are discussed.

Genetic Algorithm Based Prototype Filter Design for Oriented Side Lobe Energy Suppression in FBMC System

The prototype filter design problem is investigated for the filter bank multicarrier (FBMC) system of the fifth generation (5G) physical-layer wireless communications. In order to further suppress the side lobe energy within a certain frequency range, different constraint factors need to be introduced to meet the various side lobe energy suppression demands. In this paper, we formulate a dual-objective optimization problem which minimizes the stopband energy with constrained factors and subjects to the ISI/ICI constraints. Considering the uncertain constrained factors, a suboptimization problem is proposed by constraining the total stopband energy and the side lobe energy of the first segment to minimize the side lobe energy of the second or the third segments. Then, the nested sequential quadratic program-genetic algorithm (NSGA), one of the artificial intelligence (AI) aided algorithms, is introduced to obtain the optimal solution of the dual-objective problem, in which the genetic algorithm (GA) is applied to acquire the optimal constrained factors and the sequential quadratic program (SQP) is applied to acquire the optimal filter coefficients. Numerical results validate that the proposed method can achieve orientated side lobe energy suppression at specified segments for satisfying different side lobe energy suppression requirements with the confirmed algorithm convergence.

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences.

Experimental study on using electromagnetic devices on bridge stay cables for simultaneous energy harvesting and vibration damping

Flexible bridge stay cables are often vulnerable to problematic vibrations under dynamic excitations. However, from an energy perspective, such excessive vibrations denote a green and sustainable energy source to some electronic devices (such as semi-active dampers or wireless sensors) installed on the same cables. This paper presents an experimental study on a novel dual-function system called electromagnetic damper cum energy harvester (EMDEH). The proposed EMDEH, consisting of an electromagnetic device connected to an energy-harvesting circuit (EHC), simultaneously harvests cable vibration energy and provides sufficient damping to the cables. A fixed-duty-cycle buck–boost converter is employed as the EHC, which emulates a resistive load and provides approximately optimal damping and optimal energy harvesting efficiency when operating in discontinuous conduction mode. A 5.85 m long scaled stay cable installed with a prototype EMDEH is tested in the laboratory under a series of harmonic and random excitations. The EMDEH can achieve a control performance comparable to passive viscous dampers. An average electrical power of 31.6 and 21.51 mW is harvested under harmonic and random vibrations, respectively, corresponding to the efficiency of 16.9% and 13.8%, respectively. Moreover, this experimental study proves that optimal damping and energy harvesting can be achieved simultaneously, which answers a pending question regarding such a dual-objective optimization problem. Self-powered semi-active control systems or wireless sensor networks may be developed for bridge stay cables in the future based on the proposed concept in this study.

A dual-objective metaheuristic approach to solve practical pollution routing problem

With the growing public awareness of global warming, green supply chain management has received much attention. In particular, reducing the fuel consumption of trucks has been the topic of interest for both transportation providers (carriers) and manufacturing companies (shippers) over many years. As a way to reduce trucks’ fuel burn, attention has been given lately to the development of a decision model (problem) that is designed to solve a variant of the standard vehicle routing problem, called the pollution routing problem (PRP), which minimizes the fuel burn or pollutants emission of trucks. While useful in many respects, existing PRP models require many inputs from the users, making it inconvenient to utilize them in the field. In this paper we consider the PRP from the users’ viewpoint. We first identify, by reviewing the PRP literature and obtaining expert opinions from carrier managers, a practical PRP model that uses only a subset of the inputs required by other PRPs, and then develop an effective solution technique for this model. Based on the theoretical finding that the proposed PRP, which is a single-objective optimization problem, can be re-formulated as a dual-objective optimization problem, we develop a solution technique that utilizes the concept of efficient frontier to identify and search the most promising area of the solution space. Computational testing conducted with standard instances taken from the literature demonstrates the potential of the proposed approach.

A Crowd Wisdom Management Framework for Crowdsourcing Systems

A great many online users, with diverse backgrounds, act as powerful resources that mobile social networks (MSNs) can utilize for crowdsourcing. Exploiting these online users as crowd workers is promising yet nontrivial. To efficiently leverage human intelligence or crowd wisdom, we need to address the following issues: 1) how to motivate users to participate and 2) how to discourage malicious behaviors, such as copying answers or making random guesses. Furthermore, as low-quality answers may degrade the accuracy of synthetic results sharply, the last issue is how to weed them out. In this paper, we present MacroWiz, a simple yet effective framework to manage crowd wisdom on MSNs. Given a crowdsourcing task, MacroWiz motivates online users to contribute their knowledge or opinions, and assists the task requester in collecting answers, selecting reliable ones, and making ultimate decisions. The platform consists of two functional units: wisdom collection and answer selection. The former estimates and gathers the minimum number of answers required to satisfy the task requirement conservatively, while the latter analyzes the accuracy, the effectiveness, and the cost of each answer, based on which it selects those with high accuracy and low cost by solving a dual-objective optimization problem. We validate the effectiveness of our platform by using MovieLens data sets, which contain over one million anonymous ratings of movies. The experimental results show that MacroWiz significantly reduces the latency in making decisions and provides high-quality answers with low cost.

Face feature extraction and recognition based on discriminant subclass-center manifold preserving projection

Manifold learning is an effective dimensional reduction technique for face feature extraction, which, generally speaking, tends to preserve the local neighborhood structures of given samples. However, neighbors of a sample often comprise more inter-class data than intra-class data, which is an undesirable effect for classification. In this paper, we address this problem by proposing a subclass-center based manifold preserving projection (SMPP) approach, which aims at preserving the local neighborhood structure of subclass-centers instead of given samples. We theoretically show from a probability perspective that, neighbors of a subclass-center would comprise of more intra-class data than inter-class data, and thus is more desirable for classification. In order to take full advantage of the class separability, we further propose the discriminant SMPP (DSMPP) approach, which incorporates the subclass discriminant analysis (SDA) technique to SMPP. In contrast to related discriminant manifold learning methods, DSMPP is formulated as a dual-objective optimization problem and we present analytical solution to it. Experimental results on the public AR, FERET and CAS-PEAL face databases demonstrate that the proposed approaches are more effective than related manifold learning and discriminant manifold learning methods in classification performance.

Electromagnetic Interference-Aware Transmission Scheduling and Power Control for Dynamic Wireless Access in Hospital Environments

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.