Multi-constraint Optimization Research Articles

Floorplan-aware analog IC sizing and optimization based on topological constraints

This paper presents a methodology for analog IC circuit-level sizing and optimization, which takes into account the layout geometrical properties, by introducing a simple and general description that permits the inclusion of the floorplan generation in the sizing optimization loop with negligible computational costs. The usage of a modified NSGA-II state-of-the-art multi-objective multi-constraint optimization kernel enables the efficient exploration of design tradeoffs, while the inclusion of corner cases and the usage of the industrial circuit simulators (HSPICE®, Eldo® or Spectre®) ensures the accuracy and reliability of the solutions. Several layout templates that enclose the constraints defined by the designer are used to generate multiple floorplan solutions for each sizing solution during the synthesis process, giving the optimizer pertinent and accurate geometric layout information, e.g., area, width, length, wasted area, etc. Additionally, a built-in technology independent module generator facilitates the instantiation of multiple versions of each device, further increasing the exploration of possible geometric combinations and consequently packing of the floorplan with a minimum of wasted area. The developed tool, AIDA-C, implements the proposed approach, and is validated for both classical and new analog circuit structures using the UMC 130nm design process.

QoS Aware Geographic Opportunistic Routing in Wireless Sensor Networks

QoS routing is an important research issue in wireless sensor networks (WSNs), especially for mission-critical monitoring and surveillance systems which requires timely and reliable data delivery. Existing work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. In this work, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in WSNs. Existing GOR protocols are not efficient for QoS provisioning in WSNs, in terms of the energy efficiency and computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we define the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. Based on the analysis and observations of different routing metrics in GOR, we then propose an Efficient QoS-aware GOR (EQGOR) protocol for QoS provisioning in WSNs. EQGOR selects and prioritizes the forwarding candidate set in an efficient manner, which is suitable for WSNs in respect of energy efficiency, latency, and time complexity. We comprehensively evaluate EQGOR by comparing it with the multipath routing approach and other baseline protocols through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate the effectiveness of the GOR approach for QoS provisioning in WSNs. EQGOR significantly improves both the end-to-end energy efficiency and latency, and it is characterized by the low time complexity.

Transmit power allocation algorithm in cognitive radio MISO system based QPSK constellation

This paper considers a spectrum-sharing cognitive radio (CR) multiple-input–single-output (MISO) system which consists of a secondary user transmitter (SU-Tx) having multiple transmit antennas, K secondary user receivers (SU-Rxs) each having a single antenna, and a primary user (PU) having a single antenna. The channel state information (CSI) for the links between SU-Tx and SU-Rxs are assumed to be perfectly known at the SU-Tx. To ensure the quality of service (QoS) of PU and maximize channel capacity of SU, we formulate the channel capacity as a multiconstraint optimization problem and propose a power allocation algorithm based on QPSK constellation to solve the problem. Finally, numerical simulations are conducted to corroborate our theoretical results. It is shown that the proposed algorithm is capable of achieving a large channel capacity with fast convergence. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An Evolutionary Approach for Question Selection from a Question Bank

The success of any educational program depends on its evaluation system. Examinations are a part of learning process which acts as an element in evaluation. For the smooth conduct of examinations of various universities and academic institutions, the question paper generation process would be helpful. However, examination question paper composition is a multi-constraint concurrent optimization problem. Question selection plays a key role in question paper generation systems. Also it is the most significant and time consuming activity. The question selection is handled in traditional question paper generation systems by using a specified question paper format containing a listing of weightages to be allotted to each unit/module of the syllabus. They do not consider other constraints such as total time duration for completion of the paper, total number of questions and the difficulty level of the questions in the question paper etc. In this paper, the authors have modeled the Question Selection Problem as a multi-constraint optimization problem and proposed an Evolutionary Approach for the implementation same. The authors have experimented with this approach on a case study. The results obtained in this case study are interesting and promising to continue its implementation work and further research.

Optimal Network Design for Consensus Formation: Wisdom of Networked Agents

The wisdom of crowds refers to the phenomenon in which the collective knowledge of a community is greater than the knowledge of any individual. This paper proposes a network design for the fastest and slowest consensus formation under average node degree restrictions, which is one aspect of the wisdom of crowds concept. Consensus and synchronization problems are closely related to variety of issues such as collective behavior in nature, the interaction among agents as a matter of the robot control, and building efficient wireless sensor networks. However, designing networks with desirable properties is complex and it may pose a multi-constraint and multi-criterion optimization problem. For the purpose of realizing such efficient network topology, this paper presents an optimization approach to design networks for better consensus formation by focusing on the eigenvalue spectral of Laplacian matrix. In both the fastest and slowest networks presented, consensus is formed among local structures first, then on a global scale. This suggests that both local and global topology influence the networks dynamics. These findings are useful for those who seek to manage efficient consensus and synchronization in a setting that can be modeled as a multi-agent system.

A new multiconstraint method for determining the optimal cable stresses in cable-stayed bridges.

Cable-stayed bridges are one of the most popular types of long-span bridges. The structural behaviour of cable-stayed bridges is sensitive to the load distribution between the girder, pylons, and cables. The determination of pretensioning cable stresses is critical in the cable-stayed bridge design procedure. By finding the optimum stresses in cables, the load and moment distribution of the bridge can be improved. In recent years, different research works have studied iterative and modern methods to find optimum stresses of cables. However, most of the proposed methods have limitations in optimising the structural performance of cable-stayed bridges. This paper presents a multiconstraint optimisation method to specify the optimum cable forces in cable-stayed bridges. The proposed optimisation method produces less bending moments and stresses in the bridge members and requires shorter simulation time than other proposed methods. The results of comparative study show that the proposed method is more successful in restricting the deck and pylon displacements and providing uniform deck moment distribution than unit load method (ULM). The final design of cable-stayed bridges can be optimised considerably through proposed multiconstraint optimisation method.

Research on Application of Artificial Immune Clone Algorithm in Mission Planning of Aerospace Warfare

According to the dynamic characteristic in battlefield environment and aerospace warfare, the paper proposed the multi-objective function of mission planning based on operational efficiency and the robustness index-residual velocity impulse. The model of mission planning, which was a multi-parameter, multi-constraint and multi-objective optimization problem, was established based on the research of the time constraint and energy constraint. Meanwhile, the paper designed the immune clone algorithm which was propitious to mission planning of aerospace warfare. Simulation results indicated that the model based on immune clone algorithm can solve the mission planning of aerospace warfare effectively.

Multi-constraint hierarchical optimization combined with forecast calculation used in intelligent strategy of generating test paper

Multi-constraint constrains and reduces the test success rate,and it is difficult to make the knowledge points uniformly and automatically distributed in intelligent generating test paper.To solve these above problems,a multi-constraint hierarchical optimization strategy was proposed.It used hierarchical method to reduce the problem size,and used the tree structure to manage knowledge points and realize uniform distribution of knowledge points.With regard to the low success rate and efficiency of small test bank in generating test paper,a forecast calculation algorithm without backtracking was put forward based on hierarchical optimization algorithm to increase the test success rate.The experimental results indicate that the algorithm is suitable for large,medium and small question database,and all of them have good results.

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Improving Systemwide Sustainability in Pavement Preservation Programming

Highway agencies seek the participation of the private sector in pavement preservation, to raise revenue, share risks, and reduce project delivery costs. With public-private partnerships (PPP) representing an increasingly common approach for project delivery, recent studies have highlighted the potential of PPP and hybrid contracting (a combination of outsourcing and in-house preservation options) in enabling project-level cost savings. This paper investigates in-house and PPP practices in pavement preservation programming, and proposes a network-level hybrid approach that beneficially combines the two. Using a mixed integer programming-based optimization model and predetermined pavement preservation strategies, a holistic multiyear, multiconstraint, network-scale pavement management system methodology is described and compared with the commonly used year-by-year approach. The goal is to identify the set of recommended in-house and PPP strategies applied to a set of individual road sections, which improve the infrastructure system’s sustainability by minimizing the overall preservation cost or maximizing the condition-based benefit across the network, subject to some desired constraints, applied simultaneously. Using data from Indiana, the benefits of the multiyear, multiconstraint optimization are demonstrated, and the results show significant network-level cost savings of the proposed hybrid approach over the in-house and PPP approaches.

CBC: Caching for cloud-based VOD systems

Cloud-based video on demand (VOD) service is a promising next-generation media streaming service paradigm. Being a resource-intensive application, how to maximize resource utilization is a key issue of designing such an application. Due to the special cloud-based VOD system architecture consisting of cloud storage cluster and media server cluster, existing techniques such as traditional caching strategies are inappropriate to be adopted by a cloud-based VOD system directly in practice. Therefore, in this study, we have proposed a systemic caching scheme, which seamlessly integrates a caching algorithm and a cache deployment algorithm together to maximize the resources utilization of cloud-based VOD system. Firstly, we have proposed a cloud-based caching algorithm. The algorithm models the cloud-based VOD system as a multi-constraint optimization problem, so as to balance the resource utilization between cloud storage cluster and media server cluster. Secondly, we have proposed a cache deployment algorithm. The algorithm further manages the bandwidth and cache space resource utilization inside the media server cluster in a more fine-grained manner, and achieves load balancing performance. Our evaluation results show that the proposed scheme enhances the resource utilization of the cloud-based VOD system under resource-constrained situation, and cuts down the reject ratio of user requests.

Application Research of Improved Ant Colony Algorithm in Hybrid Mobile Agent Routing

Mobile agent routing essentially is a multi-constraint optimization problem, for Ant Colony algorithm for global search capability is weak, the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose an improved Ant Colony Algorithm for WSN mobile agent routing. This algorithm takes full use of the wireless sensor node energy, the direction of ants is determined according to the residual energy and the distance of nodes, quickly find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, effectively alleviate network congestion, and extend the network lifetime.

The Complex Blind Deflation Algorithm Based Particle Swarm Optimization with Survival of the Fittest Mechanism

For multi-constraint nonlinear optimization, this paper puts forward a complex blind deflation algorithm based particle swarm optimization with survival of the fittest mechanism (CBD-PSOSFM) which has faster convergence speed, and then gives a quantificational formula of the improved convergence speed, discusses implement method and the rule of parameters design; Because of the blind source separation (BSS) optimization characteristic in nature, the algorithm can be used to implement semi-BSS with nonlinear multi-constraint. For active object echo detection, the paper sets up fitness function with the multi-constraint like as kurtosis, energy and outline and forms the complex blind deflation algorithm. Finally, the simulation experiment of blind deflation to complex echo validates the algorithms validity and faster convergence capability.

Aircraft Climb Trajectory Prediction Using Neural Network

Aircraft climbing problem is a typical hard multi-constraint optimization problem. In real applications, it is not most important to find the best solution but to provide a feasible climbing schedule in an acceptable time. Scheduling aircraft climbing is a complex task encountered by most of the control towers. In this paper, we study the aircraft climbing problem in the multiple runway case. This paper proposes a method based on neural network which can effectively solve the aircraft climbing problem while satisfying the real-time need.

Research of Hybrid Mobile Agent Routing in Wireless Sensor Network

The mobile agent route is essentially a multi-constraint optimization problem, Genetic Algorithms has fast random global search ability, but the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose a genetic hybrid ant colony algorithm for WSN mobile agent route. Use of the fast random global search capabilities of genetic algorithm to find better solutions, then the better solution replaced by the initial pheromone of the ant colony algorithm, finally use the advantages of convergence speed of ant colony algorithm to find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, improving the speed and efficiency of data transfer.

Multi-Constraint Topology Optimization of Forging Machine Bed for Light Weight Design

Multi-Constraint Topology Optimization of Forging Machine Bed for Light Weight Design

Effective Method for Load Restoration

In our country power demand is a major problem so we are going to introduce an algorithm based on genetic algorithm is proposed to solve service restoration problem in power distribution systems with priority customers. In genetic algorithm new solutions are found in every next generation with probability of getting the better solution till the optimal solution is not found. Service restoration problem is formulated as multi objective multi-constraint combinatorial optimization problem. The integer permutation scheme is used to generate the offspring in genetic algorithm. Integer permutation does not allow the hamming cliff problem. In case of partial restoration, including micro grid action that increases the power transfer capacity reduces out-of-service area. The operating time of manual and automatic controlled switches is significantly different. Therefore both type of switches are considered separately. The comprehensive simulation results of proposed method including three phase and load side fault are presented for 5 bus test systems.

Multiobjective Service Restoration Considering Primary Customers using Hybrid GA-ACO Algorithm

To restore electricity service in healthy area outside the faulted zone in power distribution system is known as service restoration. In this paper, a technique based on hybrid Genetic Algorithm-Ant Colony Optimization (GA-ACO) is presented for solving the service restoration problem in electric power distribution system. Due the presence of various conflicting objectives and constraints, the task of service restoration is multi-objective and multi-constraint optimization problem. In contrast to conventional optimization technique, this approach does not require tunable weighting factors to convert multiple objectives into equivalent single objective. In this work various practical distribution systems issues such as presence of priority customers, remotely and manually controlled switches etc have been considered as objective functions. Based on simulation results on four different distribution networks, the performance of hybrid GA-ACO is found significantly better than Non Dominated Sorting of Genetic Algorithm-II (NSGA-II) and conventional Genetic Algorithm.

The Research of Aircraft Landing Problems Based on Neural Network

Aircraft Landing Scheduling (ALS) problem is a typical hard multi-constraint optimization problem. In real applications, it is not most important to find the best solution but to provide a feasible landing schedule in an acceptable time. Scheduling aircraft landing is a complex task encountered by most of the control towers. In this paper, we study the aircraft landing problem (ALP) in the multiple runway case. This paper proposes a method based on neural network which can effectively solve the ALS while satisfying the real-time need.

On the Sum Rate Capacity of MIMO Broadcast Channels in Cognitive Radio Networks with Interference Power Constraints

This paper investigates the sum rate capacity of MIMO broadcast channels (MIMO-BCs) in cognitive radio networks. A suboptimal user-selection algorithm is proposed to achieve a large sum rate capacity with reduced complexity. This algorithm consists of two steps. First, zero-forcing beamforming is utilized as a downlink precoding technique that precancels inter-user interference. Second, singular value decomposition is applied to the channel matrices of all the secondary users and only consider the singular vectors corresponding to the maximum singular values. The proposed user-selection algorithm chooses singular vectors which are nearly orthogonal to each other and nearly orthogonal to the vector of primary users. With this algorithm, the sum rate capacity of MIMO-BCs in CR networks with interference power constraints and transmit power constraints is derived. We formulate the sum rate capacity as a multi-constraint optimization problem and develop an algorithm to solve the problem in its equivalent form. Finally, numerical simulations are conducted to corroborate our theoretical results in flat Rayleigh fading environments. It is shown that the proposed algorithms are capable of achieving a large sum rate capacity with a very low complexity.