Grid Implementation of a Parallel Multiobjective Genetic Algorithm for Optimized Allocation of Chlorination Stations in Drinking Water Distribution Systems: Chojnice Case Study

Solving multiobjective optimization problems requires suitable algorithms to find a satisfactory approximation of a globally optimal Pareto front. Furthermore, it is a computationally demanding task. In this paper, the grid implementation of a distributed multiobjective genetic algorithm is presented. The distributed version of the algorithm is based on the island algorithm with forgetting island elitism used instead of a genetic data exchange. The algorithm is applied to the allocation of booster stations in a drinking water distribution system. First, a multiobjective formulation of the allocation problem is further enhanced in order to handle multiple water demand scenarios and to integrate controller design into the allocation problem formulation. Next, the new grid-based algorithm is applied to a case study system. The results are compared with a nondistributed version of the algorithm.     

Energy-Efficient Routing for Signal Detection in Wireless Sensor Networks

For many envisioned applications of wireless sensor networks (WSNs), the information processing involves dealing with distributed data in the context of accurate signal detection and energy-efficient routing, which have been active research topics for many years, respectively. In this paper, we relate these two aspects via joint optimization. Considering the scenario of using distributed radar-like sensors to detect the presence of an object through active sensing, we formulate the problem of energy- efficient routing for signal detection under the Neyman–Pearson criterion, apparently for the first time. The joint optimization of detection and routing is carried out in a fusion center which precomputes the routes as a function of the geographic location to be monitored. Accordingly, we propose three different routing metrics that aim at an appropriate tradeoff between the detection performance and the energy expenditure. In particular, each metric relates the detection performance explicitly in terms of probabilities of detection and false alarm, with the energy consumed in sensing and routing. The routing problems are formulated as combinatorial optimization programs, and we provide solutions drawing on operations research. We present extensive simulation results that demonstrate the energy and detection performance tradeoffs for each proposed routing metric.      

Optimal Advertising Campaign Generation for Multiple Brands Using MOGA

The paper proposes a new modified multiobjective genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic; and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using the real data sets from the Canadian advertising industry.     

A cross-layer framework for multiobjective performance evaluation of wireless ad hoc networks

In this paper we address the problem of finding the optimal performance region of a wireless ad hoc network when multiple performance metrics are considered. Our contribution is to propose a novel cross-layer framework for deriving the Pareto optimal performance bounds for the network. These Pareto bounds provide key information for understanding the network behavior and the performance trade-offs when multiple criteria are relevant. Our approach is to take a holistic view of the network that captures the cross-interactions among interference management techniques implemented at various layers of the protocol stack (e.g. routing and resource allocation) and determines the objective functions for the multiple criteria to be optimized. The resulting complex multiobjective optimization problem is then solved by multiobjective search techniques. The Pareto optimal sets for an example sensor network are presented and analyzed when delay, reliability and energy objectives are considered.     

Interactive Optimization of System Reliability Under Multiple Objectives

This paper considers a multiobjective reliability allocation problem for a series system with time-dependent reliability. The method determines the most preferable reliability allocation and preventive maintenance schedule. The problem is multiobjective nonlinear mixed-integer. The decision making procedure is based on interactive optimization and a nonlinear programming algorithm. The method is illustrated by a numerical example.     

Online multiobjective Pareto optimal dynamic scheduling of semiconductor back-end using conjunctive simulated scheduling

A discrete event simulation based "online near-real-time" dynamic multiobjective scheduling system has been conceptualized, designed, and developed to achieve Pareto optimal solutions in a complex manufacturing environment of semiconductor back-end. Our approach includes the use of linear weighted aggregation optimization approach for multiple objectives and auto simulation model generation for online simulation. Developed concepts are implemented at a semiconductor back-end site and are in use. The impact of the system includes a better customer delivery achievement, consistent cycle time with narrower distribution, improved machine utilization, reduction in the time that planners and manufacturing personnel spend on scheduling, and more predictable and repeatable manufacturing performance. In addition, it enables managers and senior planners to carry out "what now" analysis to make effective current decisions and "what if" analysis to plan for the future.     

Adaptive subcarrier and bit allocation based on ant colony optimization

The problem of resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) system is a combinatorial optimization problem, difficult to obtain optimal solutions in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, an algorithm based on ant colony optimization (ACO), which is a typical algorithm of metaheuristic methods, is proposed for the problem, utilizing excellent search performance of ACO to obtain good solutions. In addition, a parameter is applied to balance the efficiency and fairness of resource allocation. Performance analysis between algorithms based on ACO and genetic algorithm (GA) is carried out, indicating that the proposed algorithm based on ACO outperforms traditional linear algorithms as well as GA in the system throughput with assurance of fairness simultaneously, being as a promising technology for OFDM resource allocation.     

基于量子布谷鸟搜索的认知无线网络频谱分配

为了有效解决认知无线网络频谱分配的离散优化问题,将量子计算引入布谷鸟搜索算法,提出了一种新的组合优化算法——量子布谷鸟搜索算法。该算法使用量子鸟窝表征问题的多维解,通过Lévy flights随机游动方式和量子突变策略快速搜索到全局最优位置。通过使用基准函数验证了算法的高效性,并提出了一种基于量子布谷鸟搜索的认知无线网络频谱分配方法。然后与经典频谱分配算法在不同的网络效益函数下进行仿真性能比较。结果表明,所提出的频谱分配方法能够较快找到全局最优解,并且在不同网络效益函数下均优于已有的经典频谱分配算法。     

基于多维离散粒子群优化的协同 OFDMA 系统跨层资源分配

摘 要：针对多服务情况下协同OFDMA（orthogonal frequency division multiple access）系统的资源分配问题,在基站和中继单独功率约束条件下,以最大化用户的效用(utility)总和为目标,提出了一种基于多维离散粒子群（MDPSO）的渐进最优资源分配算法。该算法采用多值离散变量来编码粒子位置,并针对多维离散空间构建了新的基于概率信息的粒子速度和位置更新算法,且引入变异操作来克服粒子群算法的早熟问题。此外,还采用了迭代注水法进行最优功率分配。仿真结果表明,所提算法在总效用、吞吐量和公平性上均明显优于已有资源分配算法。     

Fuzzy logic approach to VLSI placement

A contemporary definition of VLSI placement problem is characterized by multiple objectives. These objectives are: timing, chip area, interconnection length and possibly others. In this paper, fuzzy logic has been used to facilitate multiobjective decision-making in placement for standard cell design style. A placement process has been defined in terms of linguistic variables, linguistic values and membership functions. Various objectives have been related by hierarchical fuzzy logic rules implemented as object-oriented programming objects. It is demonstrated that a designed fuzzy logic system is flexible in selecting goals and considering tradeoffs. Details of implementation, experimental results and comparisons with other systems are provided     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

一种融合噪声网络的深度强化学习通信干扰资源分配算法

针对传统干扰资源分配算法在处理非线性组合优化问题时需要较完备的先验信息,同时决策维度小,无法满足现代通信对抗要求的问题,该文提出一种融合噪声网络的深度强化学习通信干扰资源分配算法(FNNDRL)。借鉴噪声网络的思想,该算法设计了孪生噪声评估网络,在避免Q值高估的基础上,通过提升评估网络的随机性,保证了训练过程的探索性;基于概率熵的物理意义,设计了基于策略分布熵改进的策略网络损失函数,在最大化累计奖励的同时最大化策略分布熵,避免策略优化过程中收敛到局部最优。仿真结果表明,该算法在解决干扰资源分配问题时优于所对比的平均分配和强化学习方法,同时算法稳定性较高,对高维决策空间适应性强。     

Virtual network function–forwarding graph embedding: A genetic algorithm approach

Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time.     

Design of Mechatronic Systems With Configuration-Dependent Dynamics: Simulation and Optimization

This paper considers the simulation and optimization of mechatronic systems with configuration-dependent dynamics. A modeling methodology, able to capture the varying dynamics and the embedded control system actions, using affine reduced models and cosimulation, is proposed. In this way, mechatronic systems with configuration-dependent dynamics can be evaluated during the design phase. This methodology is applied to a pick-and-place assembly robot and an experimental validation is carried out. The mechatronic design approach, which takes into consideration structural and control parameters, is considered. Using time-domain metrics, two control strategies are derived: a linear time-invariant proportional--integral--derivative (PID) controller and a linear parameter-varying PID controller. Finally, design tradeoffs are evaluated in a truly mechatronic approach.      

Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user’s signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.     

Resource allocation in OFDMA networks based on interior point methods

This paper studies a joint optimization problem of sub‐carrier assignment and power allocation in orthogonal frequency division multiple access (OFDMA) wireless networks. A major challenge in solving the optimization problem is non‐convexity caused by the combinatorial nature of sub‐carrier assignment problem and/or non‐convex objective functions. To address the combinatorial complexity, we formulate the resource allocation problem as an optimization problem with continuous variables. We propose a novel approach based on a penalty function method and an interior point method (PM/IPM) to solve the problem. In specific, using a two‐step implementation, the penalty method is applied first to convert the non‐convex feasible region to a convex one. Then, the interior point method is deployed to solve the problem which is non‐convex only in the objective function. To evaluate the performance of PM/IPM, we apply a genetic algorithm (GA) that achieves near optimal solutions of the problem by iterative searching. Copyright © 2009 John Wiley & Sons, Ltd.     

A Decision Analysis Framework for Prioritizing a Portfolio of ICT Infrastructure Projects

The inadequacy of traditional quantitative cost–benefit analysis for evaluating information and communications technologies (ICT) infrastructure investments have led researchers to suggest real options (ROs) analysis for valuating ICT projects. However, ROs models are strictly quantitative and often, ICT investments may contain qualitative factors that cannot be quantified in monetary terms. In addition, ROs analysis results in some factors that can be treated more efficiently when taken qualitatively. This paper combines ROs and the analytic hierarchy process (AHP) into a common decision analysis framework, providing an integrated multiobjective, multicriteria model called ROAHP for prioritizing a portfolio of interdependent ICT investments. The proposed model is applied to an ICT case study showing how it can be formulated and solved.      

An Integrated Approach for Achieving Multirobot Task Formations

In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.     

Study on distributed and dynamic resource management for delay-sensitive sensor network

The delay-aware dynamic resource management problem was investigated in sensor network,with a focus on resource allocation among the sensors and power control along the time.By taking account of average delay requirements and power constraints,the considered problem was formulated into a stochastic optimization problem.Inspired by Lyapunov optimization theory,the intractable stochastic optimization problem was transformed into a tractable deterministic optimization problem,which was a mixed-integer resource management problem.By exploiting the specific problem structure,the mixed-integer resource management problem was equivalently transformed into a single variable problem,and the cooperative distributed method was present to effectively solve it with guaranteed global optimality.Finally,a dynamic resource management algorithm was proposed to solve the original stochastic optimization problem.Simulation results show the performance of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.     

Optimal testing resource allocation, and sensitivity analysis in software development

We consider two kinds of software testing-resource allocation problems. The first problem is to minimize the number of remaining faults given a fixed amount of testing-effort, and a reliability objective. The second problem is to minimize the amount of testing-effort given the number of remaining faults, and a reliability objective. We have proposed several strategies for module testing to help software project managers solve these problems, and make the best decisions. We provide several systematic solutions based on a nonhomogeneous Poisson process model, allowing systematic allocation of a specified amount of testing-resource expenditures for each software module under some constraints. We describe several numerical examples on the optimal testing-resource allocation problems to show applications & impacts of the proposed strategies during module testing. Experimental results indicate the advantages of the approaches we proposed in guiding software engineers & project managers toward best testing resource allocation in practice. Finally, an extensive sensitivity analysis is presented to investigate the effects of various principal parameters on the optimization problem of testing-resource allocation. The results can help us know which parameters have the most significant influence, and the changes of optimal testing-effort expenditures affected by the variations of fault detection rate & expected initial faults.