Multi-objective Constraint Optimization Problem Research Articles

Cooperative beamforming in cognitive radio systems without feedback of receiver beamforming vectors to transmitter

In this paper, we investigate the problem of transceiver beamforming and power allocation in the downlink of a multiple input multiple output Cognitive radio system. In this system, a base station (BS) services multiple primary users (PU) and multiple secondary users (SU). In order to perform transceiver beamforming and power allocation, three new algorithms are proposed. The first one is an iterative single-objective constrained algorithm, in which the signal to interference plus noise (SINR) of SUs are improved at each iteration subject to constraints on quality of services of PUs (PUs SINR should be above a predefined threshold) and total transmit power of BS. The second and third algorithms, are based on a multi-objective constraint optimization problem, in which the goal is to improve both PUs and SUs SINR at each iteration subject to previous constraints of the first algorithm. The difference between the second and third algorithms is that in the joint maximization of the second algorithm, the cross-influence of each user on the others is ignored, but in the third algorithm, the cross effect of each users SINR on the others is considered. The novelty of these algorithms is the independency of the transmit precoding vectors from the receive beamforming vectors. Therefore, the feedback of receive beamforming to the BS is not needed. The performances of three proposed algorithms are evaluated in simulations vis bit error rate of PUs and SUs, SINR of PUs and SUs and the percent of BS power allocation to SUs.

Parallel robots modelling and optimization

Parallel robots are mechanical systems with numerous applications in manufacturing, medicine, space industry, etc. Such robots are characterized by closed kinematic chains ensuring robustness and maneuverability. The design of a fully functional parallel robot is a time-consuming task as an engineer needs to enumerate and compare many variants in order to find one with the best characteristics. Typical characteristics are the area of the workspace, the number of singularity-free regions, dexterity index. The design of a parallel robot is thus a multi-objective constraint optimization problem. We propose to use interval analysis to reliably compute characteristics and multiobjective optimization methods to find the Pareto-optimal set of robot parameters. The proposed approach is illustrated on a set of several realistic parallel robotic systems.

Heuristic Edge Server Placement in Industrial Internet of Things and Cellular Networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current information and communications technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile-edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay, is a challenging problem. In this article, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom's base station data set that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multiobjective constraint optimization problem that places edge servers strategically to balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hill climbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function.

Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning

Service-oriented architecture (SOA) is a significant framework that enables intelligent information systems to offer business process-based services, namely Business Process as a Service (BPaaS). Cloud service-based business model reconstruction within and across enterprises has become an important issue to obtain competitive advantages. Finding appropriate service component is a critical phase in enterprise collaboration, to improve the quality and correlations among collaborative service-providers. Existed methods for business processes reconstruction have not systematically and fully considered the quality correlations in finer-grain, i.e. task-level or activity-level, and temporal performance in a process model optimization. Moreover, the existed approaches might fail to work in an uncertain cloud environment where the quality parameters are unknown in advance. Q-learning has proven its worth in an uncertain cloud environment. However, advances in Q-learning are challenges to leverage in collaborative business process reconstruction. The reconstruction algorithms based on Q-learning suffered from the two core drawbacks: lack of effective exploration and extremely slow convergence property. A hybrid Evolutionary Deep Q-Learning-based BPaaS reconstruction algorithm, named as EDQL-BPR, is proposed by leveraging Particle Swarm Optimization to improve Deep Q-Learning algorithm for systematically optimizing collaborative business processes reconstruction. In order to verify the effectiveness of the proposed algorithm, an annotated transition system has been developed supporting for all possible behaviors state convention from heterogeneous initial states to the target with several collaborative information at activity level, and encoding annotated BP representation matrix automatically. Then, an autonomous three-layer framework has been built to facilitate business process discovery and service reconstruction. In this framework, our optimal collaborative services composition is treated as a multi-objective constraint optimization problem via Evolutionary Deep Q-learning with updated strategy of Q-value, and each annotated component launch a service discovery by service agent. Extensive evaluation results show that EDQL-BPR can outperform several representative business process reconstructions in terms of model optimal convergence, QoS optimality, effectiveness and efficiency under heterogeneous service selection workloads in an uncertain Cloud environment.

Towards Network-Aware Service Composition in the Cloud

Composing several API-defined services into one composite service per user requirements has become an important service creation approach in the cloud-enabled API economy. Various service selection approaches in support of service composition on demand have been proposed. They usually assume that networking resources are over-provisioned and their usage needs not be considered when making quality-aware service composition decisions. In practice, these approaches often lead to wasteful network resource consumption and impractical end-to-end QoS optimality for cloud-based services. This paper proposes a network-aware cloud service composition approach, named NetMIP, with comparative experimental evaluations for the clouds that adopt the widely deployed fat-tree network topology. By formalizing the service composition goal as a multi-objective constraint optimization problem, we have validated the proposed approach can be used to effectively reduce network resource consumption and deliver QoS optimality while satisfying the end-to-end QoS constraints for the candidate composite services in the cloud. The comparative experimental evaluations are done via a credible cloud infrastructure simulation system, named WebCloudSim. Extensive evaluation results show that NetMIP outperforms several representative cloud service composition approaches in terms of network resource consumption, QoS optimality, and computation time under various service selection workloads and fat-tree network topology settings.

Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper

Robotic grippers play a key player in the industrial robotics application such as pick and place, and assembly. In this article, geometric modeling of a robotic gripper is proposed and a plan is outlined to obtain optimized design parameters of the robotic gripper using various meta-heuristics techniques. The proposed system was solved in two-step methodology as geometric modeling followed by the formulation of objective functions. The developed two objective functions of the robotic gripper are complex and act as the multi-objective constraint optimization problem. Seven decision variables are chosen to develop the geometric model, and the proposed objective function for the robotic gripper is solved using different metaheuristic techniques such as ABC, FA, TLBO, ACO, and PSO algorithm. A statistical study conducted considering the 100 independent run for all the algorithms.

Edge server placement in mobile edge computing

With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing.

Trajectory Planning for Spray Painting Robot of Free-Form Surfaces

Trajectory optimization of spray painting robot for surfaces is studied. Because the current paint deposition rate function is too complicated, the paint deposition rate function on a plane according to the experiment data is provided. The paint thickness function for free-form surfaces is also given. A multi-objective constraint optimization problem is formulated. An optimal tool trajectory with an optimal time and film quantity deviation is generated.Experiments are performed to verify the generated trajectory. The results show that the developed optimal tool planning algorithm could be applied to generate optimal tool trajectories.

Path Planning for Spray Painting Robot of Workpiece Surfaces

A new optimization algorithm of the path planning for spray painting robot of workpiece surfaces is proposed. This paper first provides the paint deposition rate function on a plane according to the experiment data. And the model of film thickness on surface is discussed. A multiobjective constraint optimization problem is formulated. An optimal tool path with an optimal time and film quantity deviation is generated. And the min-max method is adopted here to calculate the values. A workpiece, which is a free-form surface, is used to test the scheme. The results of experiments have shown that the path optimization algorithm achieves satisfactory performance. This algorithm can also be extended to other applications.