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This paper addresses a UAV path planning problem for a team of cooperating heterogeneous vehicles composed of one unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV, modeled as a Dubins vehicle, serves as a messenger to fly over the effective communication range of all UGVs to relay information. The curvature-constrained path planning of the messenger UAV is formulated as a Dubins Traveling Salesman Problem with Dynamic Neighborhood (DTSPDN) which is a complex optimization problem involving coupled variables and contains dynamic constraints. We design an effective memetic algorithm to find the shortest route that enables the messenger UAV to visit all moving UGVs. This algorithm combines the genetic algorithm procedure, two kinds of local search operators based on gradient search and uniform sampling respectively, and a gradient-based repair operator to repair the solutions violating dynamic constraints. During the evolutionary process, a special phenomenon may occur that changing some decision variables (i.e., visiting sequence and location) may not affect the evaluation function value, but may alter the feasible region of another decision variable (i.e., visiting time) due to the encounter constraint between the UAV and UGV. To track and utilize the change of the feasible region, a transformation procedure is proposed to change one solution to another with less visiting time by analyzing the encounter pattern between UAV and UGV. The computational results on random instances with different scales demonstrate that the proposed approach can effectively generate better curvature-constrained tours to encounter all moving UGVs when compared to other four competitive algorithms in the literature. Note to Practitioners—This paper studies an emerging path planning problem for a UAV which is used to provide communication service for multiple moving UGVs. These UGVs are required to execute tasks (e.g., firefighting, search and rescue) within a large area. Due to their limited communication capabilities, they may be unable to obtain necessary information from other UGVs. The UAV serves as a messenger to fly over the effective communication range of all moving UGVs to relay information. We propose a novel memetic algorithm to efficiently search for the shortest tour that enables the messenger UAV to visit all moving UGVs. The memetic algorithm combines the parallel global search virtue of genetic algorithm with efficient local search procedure to improve the generated tour. A gradient-based repair procedure is also employed to make sure that the planned tour can guide the UAV to sequentially encounter each moving UGV. Simulations exhibit that the proposed approach can effectively generate high-quality tours for messenger UAV to rapidly visit all UGVs, which assists UGVs to achieve collaboration in large area. In future work, the proposed memetic algorithm will be extended to plan tours for multiple messenger UAVs. [ABSTRACT FROM AUTHOR]

It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of “duplicate overrides” and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale. [ABSTRACT FROM AUTHOR]

This paper focuses on improving indoor Wi-Fi localization by mitigating the effect of fluctuation of received signal strength indication (RSSI). The RSSI data collected at each reference point is first transformed through translation and scaling. The BP (Back Propagation) neural network is then used to construct the ranging model using the transformed RSSI to determine the distances between the target point and each reference point. A genetic algorithm (GA) is developed to optimize the initial values of weights and biases of the BP neural network. For convenience, our proposed ranging model is denoted as GTBPD. A new localization algorithm is then proposed, which uses the GTBPD model and the sequential quadratic programming (SQP, an iterative nonlinear optimization algorithm), and the algorithm is denoted as GTBPD-LSQP for simplicity. Experiments were conducted in three areas of two different teaching buildings with complex environments. The performance of the proposed GTBPD-LSQP algorithm is evaluated and compared with four existing algorithms. The experimental results show that our proposed GTBPD-LSQP algorithm achieves significantly higher location accuracy than the four existing algorithms. [ABSTRACT FROM AUTHOR]

Compiling source code effectively to meet various criteria is a critical task in software engineering. Especially, code size reduction has attracted much attention from both industry and academia due to the requirement of resource utilization. Generally, developers rely on compiler optimization passes to realize code size reduction. However, it is impractical to select a desirable optimization sequence manually since a wide variety of optimization passes are integrated into a compiler. Evolutionary algorithms offer an impressive way to alleviate this problem. Nevertheless, previous approaches fail to balance the exploitation and exploration of the search space. Moreover, the expensive fitness evaluation requires actual compilation, which makes the evolution rather time-consuming. To tackle the challenges, we propose a novel approach SMARTEST, which characterizes the systematic exploitation of a huge volume of historical compilation information. Specifically, SMARTEST comprises two components: 1) a local search operator to enhance the solution quality; and 2) a data-driven surrogate model to avoid expensive fitness evaluation. We evaluate the effectiveness of SMARTEST over the cBench benchmark suite. Experimental results indicate that SMARTEST outperforms the standard level -Os by 2.17% on average, and achieves 1.2 times code size reduction compared with the genetic algorithm. Furthermore, experimental results over the benchmark suite evidently show that SMARTEST gets a better result and takes less actual fitness evaluations than its variants, which demonstrates the contribution of the local search and the surrogate model. [ABSTRACT FROM AUTHOR]

Machine learning models have gained popularity nowadays for their potential to solve real-life issues when trained on pertinent data. In many cases, the real-life data are class imbalanced and hence the corresponding machine learning models trained on the data tend to perform poorly on metrics like precision, recall, AUC, F1, and G-mean score. Since class imbalance issue poses serious challenges to the performance of trained models, a multitude of research works have addressed this issue. Two common data-based sampling techniques have mostly been proposed-undersampling the data of the majority class and oversampling the data of the minority class. In this article, we focus on the former approach. We propose two novel algorithms that employ neural network-based approaches to remove majority samples that are found to reside in the vicinity of the minority samples, thereby undersampling the former to remove (or alleviate) the imbalance issue. We delineate the proposed algorithms and then test the proposed algorithms on some publicly available imbalanced datasets. We then compare the performance of our proposed algorithms to other popular undersampling algorithms. Finally, we conclude that our proposed algorithms outperform most of the existing undersampling approaches on most performance metrics. [ABSTRACT FROM AUTHOR]

A Dandelion algorithm (DA) inspired by the seed dispersal process of dandelions has been proposed as a newly intelligent optimization algorithm. For improving its exploration ability as well as reducing the probability of its falling into a local optimum, this work proposes to add a novel competition mechanism with historical information feedback to current DA. Specifically, the fitness value of each dandelion in the next generation, which is calculated by linear prediction, is compared with the current best dandelion, and the loser is replaced by a new offspring. Current DA generates new offsprings without considering historical information. This work improves its offspring generation process by exploiting historical information with an estimation-of-distribution algorithm. Three historical information models are designed. They are best, worst, and hybrid historical information feedback models. The experimental results show that the proposed algorithms outperform DA and its variants, and the proposed algorithms are superior or competitive to nine participating algorithms benchmarked on 28 functions from CEC2013. Finally, the proposed algorithms demonstrate the effectiveness on four real-world problems, and the results indicate that the proposed algorithms have better performance than its peers. [ABSTRACT FROM AUTHOR]

In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter. [ABSTRACT FROM AUTHOR]

In this article, a novelvertical wire-bonding interconnect structure is intelligently modeled and optimized with general artificial neural network (GRNN) and genetic algorithm (GA) for multilayered system in package. A compensation structure is constructed with a hybrid inductive and capacitive technique, while a capacitive stripline with series inductive short-end via is designed underneath the 50-Ω transmission line. The GRNN algorithm is employed to build the electromagnetic model databases during the procedure of GA optimization. In comparison with the conventional optimization algorithm, the output performances can be directly achieved with collaboratively combined methods, which can significantly reduce the calculation time. From the measurement results, the return loss is improved significantly while the parasitic inductive behavior of the bonding wire is eliminated with the presented design. Moreover, compared with the traditional compensation techniques, no additional area is occupied on the surface plane of the wire-bonding interconnection, which is more suitable for the high-integrated circuit system. [ABSTRACT FROM AUTHOR]

This paper investigates the sub-channels and power allocation issue in the multi-carrier NOMA communication system. To maximize the sum rate of the system, we design a fast dynamic resource allocation (FDRA) algorithm. The algorithm can quickly identify the interference nodes with poor channel conditions, and reduces the search complexity of the algorithm. By jointly optimizing sub-carrier and power resource allocation, the proposed scheme can take full advantage of the channel capacity, and maximize the system performance while ensuring the quality of service (QoS) of each user. Simulation results show that when compared with the joint resource allocation (JRA) algorithm and the genetic algorithm (GA), the FDRA algorithm can achieve the best performance on the complexity and the sum rate of the system. Thus, the proposed algorithm can provide a fast and efficient resource allocation scheme for the downlink multi-carrier NOMA systems. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel multiobjective controller to regulate the power converters of a class of dc microgrids connected to nonlinear constant power loads and linear resistive loads. The suggested control approach uses the nondominating sorting binary genetic algorithm (NSBGA-II) to directly design the on/off switching signal of the converters without using the pulsewidth modulation technique. The multiobjective controller minimizes the tracking error of the dc bus voltage and at the same time tries to reduce the total number of switching actions. Thereby, the developed controller tracks the desired reference with a reduced converter switching action and power loss by using a proper Pareto solution. Moreover, by employing the NSBGA-II algorithm, it is feasible to involve the switching frequency in the design procedure to enhance the performance. Exploiting the binary genetic algorithm instead of the conventional genetic algorithm (GA) turns a continuous surface searching into a binary one, which not only makes it more compatible with the nature of the power converter control but also decreases the online computational burden. To illustrate the superiority of the proposed approach, real-time OPAL results are provided. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes, for a high-scoring Bayesian network. Our scoring function is derived from regularizing Gaussian DAG likelihood, and its optimization gives an alternative formulation of the sparse Cholesky factorization problem from a statistical viewpoint. We combine simulated annealing over permutation space with a fast proximal gradient algorithm, operating on triangular matrices of edge coefficients, to compute the score of any permutation. Combined, the two approaches allow us to quickly and effectively search over the space of DAGs without the need to verify the acyclicity constraint or to enumerate possible parent sets given a candidate topological sort. The annealing aspect of the optimization is able to consistently improve the accuracy of DAGs learned by greedy and deterministic search algorithms. In addition, we develop several techniques to facilitate the structure learning, including pre-annealing data-driven tuning parameter selection and post-annealing constraint-based structure refinement. Through extensive numerical comparisons, we show that ARCS outperformed existing methods by a substantial margin, demonstrating its great advantage in structure learning of Bayesian networks from both observational and experimental data. We also establish the consistency of our scoring function in estimating topological sorts and DAG structures in the large-sample limit. Source code of ARCS is available at https://github.com/yeqiaoling/arcs_bn. [ABSTRACT FROM AUTHOR]

Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of ‘AirMatrix’, the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multi-path planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators’ requests are both considered. A ‘cross-off’ strategy is developed to calculate the fitness value, and a ‘distributed-centralized’ strategy is applied considering the task priorities of drones. A genetic algorithm GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured. [ABSTRACT FROM AUTHOR]

One of the parameters required for a high-performance drive of a surface-mounted permanent magnet synchronous motor (SPMSM) operating in the flux-weakening region is the torque ripple reduction. In general, the torque ripple results from many reasons, such as the machine structure, oscillations of measured speed, etc. Hence, this article proposes an optimized strategy that works in conjunction with a variable switching frequency pulsewidth modulation algorithm to reduce the torque ripple peak. For the modulating algorithm, the updated frequency changes linearly with the desired torque ripple and its predicted maximum peak. For the proposed strategy, the reflection of proportional-integral (PI) parameters is observed at the torque ripple caused by the current ripples. Therefore, a robust method for adjusting PI parameters is proposed that relies on the improved fitness function that can minimize the regulators' error and maximize the drive stability bandwidth in the flux-weakening region. This objective function is optimized offline using a genetic algorithm optimization technique. Meanwhile, a third-order generalized integral flux observer is applied in this article, providing evidence of torque ripple reduction. For driving in the flux-weakening region, the reference magnitude of the duty cycles can produce the required d-axis reference current to prevent the saturation of current controllers. Finally, comprehensive simulations and experiments are presented to validate the proposed strategy effectively. In comparison with the conventional method, the proposed strategy can reduce the copper and switching losses simultaneously and control the current and speed in the flux-weakening region efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings. [ABSTRACT FROM AUTHOR]

This article introduces the design and optimization of line-start synchronous machines in fractional horsepower industry applications. The proposed optimized design will be considered as a replacement for the induction machine driven from an ac drive. The optimized design will be achieved using a 2-D finite-element transient analysis and multi-objective optimization algorithm. For the optimization, a multi-objective self-organizing migrating algorithm was chosen. The optimization results are compared with the more popular nondominated sorting genetic algorithm (NSGA-II) algorithm, to prove algorithm sufficiency with three objectives and five design parameters in the area of the electromagnetic design application. [ABSTRACT FROM AUTHOR]

In this article, an optimal control strategy for a bearingless permanent magnet synchronous machine (BPMSM) drive is proposed. The state feedback control (SFC) based on the grey wolf optimization (GWO) algorithm is applied. As for the BPMSM system, coupling and nonlinearity exist, which hinders the SFC. Hence, the linearization of the BPMSM mathematical model is implemented first. Second, the discretized state model with the augmented integrals of the displacement error and the angular speed error is obtained. Then, the weighting matrices Kd are obtained by employing the GWO algorithm. Finally, simulations and experiments are carried out to verify the effectiveness of the proposed method. Comparisons between the controllers with and without the penalty term are conducted. Meanwhile, the proportional-integral (PI) controllers based on the genetic algorithm and the proposed one are compared as well. The results show the superiority of the proposed method reflecting in faster response and no overshoot compared with the PI controllers. [ABSTRACT FROM AUTHOR]

Series resonant converter (SRC) is one of the main parts which has to be designed carefully to be compatible with the general requirements of photovoltaic (PV) grid-connected system. The extensive search space for design parameters of SRC such as resonant tank component values, transformer turn ratio, dead time and switching frequency requires an automated design framework. The proposed framework is developed using genetic algorithm (GA), differential evolution algorithm (DEA), and nondominated sorting genetic algorithm (NSGA-II) for the multi-objective optimization of SRC circuit while accomplishing zero voltage switching (ZVS). Regulating the output voltage while minimizing the losses of MOSFET, diode and transformer are the design objectives. FEM is applied to subdivide the single phase transformer into simpler and finite parts to calculate flux density value according to the optimized design parameters. Amongst these evolutionary algorithms, NSGA-II achieved the best optimization performance considering various operation modes and required specifications. Simulation results are also provided to validate the efficient and robust automated design of SRC to be used in photovoltaic systems. [ABSTRACT FROM AUTHOR]

Stochastic simulation is beneficial when evaluating the performance of a complex system. When optimizing the system performance with the simulation, we need to make a final decision by considering various qualitative criteria neglected by the simulation as well as the simulation results. However, as simulations are expensive and time-consuming, in this paper, we propose a ranking and selection algorithm to make such optimization with the simulation efficient. The proposed algorithm selects a best-subset of designs expected to optimize the system performance from a finite set of alternatives. Furthermore, the algorithm identifies the ranking of designs within the subset. To maximize the accuracy of the selection under limited simulation resources, the algorithm selectively and gradually increases the precision of the sample mean of each design by allocating the resources heuristically based on the evaluated uncertainty. The selected subset allows decision makers to efficiently choose the best design that optimizes the performance while satisfying the qualitative criteria. We exhibit various experimental results, including a practical case study, to empirically demonstrate the efficiency and high noise robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Deadlock-free scheduling and control is critical for optimizing the performance of flexible assembly systems (FASs). Based on the Petri net models of FASs, this paper integrates a deadlock prevention policy with local search and develops a novel deadlock-free scheduling algorithm. A solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the deadlock prevention policy, a repairing algorithm (RA) is developed to repair unfeasible chromosomes. A perturbation strategy based on estimation of distribution algorithm is developed to escape from local optima. Moreover, to improve population diversity, an acceptance criterion (AC) based on Pareto dominance is proposed. The chromosome representation, RA, perturbation strategy, and AC together support the cooperative aspect of local search for scheduling problems strongly. [ABSTRACT FROM AUTHOR]

The computational efficiency of large-scale unit commitment (UC) is still a critical issue in power system and electricity market operations. To reduce the computation time of UC, relaxation-based neighborhood search (RBNS) and improved relaxation inducement (IRI) are proposed in this article. RBNS explores the neighborhood of the linear program (LP) relaxation optimal solution for a high-quality feasible solution. A new distance function, termed relaxation distance (RD), is proposed to measure the distance between the current solution and the tendency of the LP relaxation optimal solution. RBNS can substantially reduce the optimization space, and thus improve the efficiency. IRI has been developed to effectively induce binary variables towards the tendency of the relaxed solution. In contrast to a conventional relaxation inducement method, the binary variables are symmetrically and bi-directionally induced. The ratio between the inducing functions and the original objective function is optimized. IRI can induce more binary variables to integrality, and fewer binary variables need to be branched. Therefore, the size of the branch-and-bound tree can be reduced significantly. Modified IEEE-300 bus system and Polish 2746 bus system are used to demonstrate the effectiveness and performance of the proposed RBNS and IRI methods. [ABSTRACT FROM AUTHOR]

To adapt to the uncertain environment smartly and timely, cyber physical systems (CPSs) have to interact with the physical world in a decentralized but rigorous, organized way. Guaranteeing the timing reliability is key to achieve consensus on the order of distributed events, as well as dependable cooperative decision processing. Based on our hierarchically decentralized compositional self-adaptive framework, we propose a formal compositional reliability-contract-based solution to guarantee the timing reliability of event observation and decision processing in a large-scale, geographically distributed CPS. As the prophetic decision may not fit the local situation well because of the uncertainties, we propose a gradual contract optimization solution to refine the dependability, timeliness, and energy consumption. Following the seven proposed composition schemes, we employ the nondominated sorting genetic algorithm II (NSGA-II) algorithm to optimize arrangement of decision. Moreover, a topology-aware time reserving solution is applied to improve the resilience of processing time and to tolerance timing failures. Both simulation results and real-world testing are introduced to evaluate the efficacy of our proposal. We believe that the formal compositional contract will be a competitive CPS solution to analyze requirements and optimize the self-adaptation decision at runtime. [ABSTRACT FROM AUTHOR]

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint optimization between the offloading decisions of UTDs and the allocation of resources in network is still a focus of the research. This paper studies the joint computation offloading and resource allocation strategy in multi-user and multi-server scenarios. Firstly, we formulate the joint optimization problem of computation offloading and resource allocation as a mixed integer nonlinear programming (MINP) problem to minimize the energy consumption of UTDs, by constraining the offloading decision, channel selection, power allocation and resource allocation. Secondly, we propose a two-stage heuristic optimization algorithm based on genetic algorithms, which divides the joint optimization problem of computation offloading and resource allocation in two stages. Based on the coupling relationship between the offloading decision and the resource allocation scheme, we iteratively update the solution of the problem, and finally obtain the stable convergence solution of the optimization problem. Finally, the proposed algorithm is compared with other classical methods to prove the effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases. [ABSTRACT FROM AUTHOR]

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches. [ABSTRACT FROM AUTHOR]

Fault diagnosis has a critical role in maintaining the reliability and stability of distribution networks. Compared with common diagnosis methods, such as expert systems that depend on the professional experience and information of fault, the model-based diagnosis (MBD) has many distinct advantages. However, the efficiency and accuracy of the calculation of the minimum hitting set (MHS) limit the performance of MBD. To overcome these limitations, an MHS algorithm with a prejudging mechanism is proposed in this article. This mechanism is constructed based on the distribution characteristics of MHS. The use of this mechanism prior to MHS calculation enables the removal of most non-HS particles and all super-MHS particles and enables the compression of the MHS calculation solution space several times. Moreover, after compression, only two types of particles remain in the left solution space, which are non-HS and MHS. As a result, the MHS can be picked out directly through the HS fitness function and the HS criterion and by avoiding the participation of the MHS ensured strategy (MHSES). Three cases studies have been introduced to test the effect of the prejudging mechanism on the performance of the MHS calculation. In addition, a discussion on the MBD of the distribution networks has been presented to validate the efficiency and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article proposes a modified grey wolf optimization (MGWO) which has improved rate of convergence and avoids local optima stagnation. This is achieved by improving the exploration–exploitation balance in conventional grey wolf optimization (GWO). Moreover, the use of weighted-distance strategy allows quick identification of the global optima in the proposed MGWO. This optimization is then applied for the control of a three-phase, 11-level hybrid cascaded multilevel inverter (HC-MLI). Selective harmonic elimination pulsewidth modulation technique is implemented through MGWO which generates optimal switching angles for the HC-MLI so as to eliminate lower order harmonics such as 5th, 7th, 11th, and 13th from the output voltage. In MGWO optimized HC-MLI, balancing of capacitor voltage is made possible at higher modulation index by making use of the available redundant switching states. The performance of the proposed work is validated through simulation and experimentation on a 1.5 kW prototype. The results obtained through simulation show that the MGWO algorithm is more efficient and accurate than other reported algorithms such as GWO, genetic algorithm, and particle swarm optimization in terms of performance, harmonic reduction, and convergence rate. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is a newly developed swarm intelligence-based optimization technique that mimics the social hierarchy and group hunting behavior of grey wolves in nature. Here, a detailed introduction of the GWO algorithm is given, after which, three sets of examples are investigated: first, numerical experiments on four benchmark functions are conducted; second, the GWO is applied to the synthesis of linear arrays with the aim of reducing the peak sidelobe level under various constraints; and finally, the performance of the GWO is further verified on the optimization design of two representative antennas, namely, a dual-band E-shaped patch antenna and a wideband magneto-electric dipole antenna. The results show that the GWO is capable of outperforming or providing very competitive results compared with some well-known metaheuristics such as the genetic algorithm, particle swarm optimization, and differential evolution. Thus, it may serve as a promising candidate for handling electromagnetic problems. [ABSTRACT FROM AUTHOR]

In this paper, a nondominated sorting genetic algorithm (NSGA)-II-based calibration algorithm for the underwater binocular vision measurement system is proposed. The underwater measurement results will be incorrect with the binocular camera model in air. To deal with this problem, a refractive camera model and an akin triangulation are proposed to establish the nonlinear relationship with housing parameters between the object and its corresponding image plain points. A novel usage of checkerboard based on the relative position relationship of corners is employed to set three optimal goals, i.e., the distance difference, the vertical direction difference, and the parallel direction difference. The process of calibration is regarded as a multiobjective optimization and solved by NSGA-II. Finally, experimental results demonstrate the validity and effectiveness of the proposed calibration algorithm. [ABSTRACT FROM AUTHOR]

Space debris has become a great threat to space activities. We study and optimize space missions to remove multiple space debris. These missions are efficient and economical in stabilizing the orbital environment. In this paper, we propose an intelligent global optimization algorithm named the evolving elitist club algorithm (EECA), and we study its application in multiple debris removal missions. The crucial design element of EECA is the elitist club, which is a population created using ant colony optimization (ACO) and then evolved using genetic algorithm operators. We describe two types of multiple debris removal missions: 1) single spacecraft removal mission; and 2) multiple spacecraft removal mission. We optimize them using EECA. The proposed algorithm is compared with the conventional tree search method and other ACO algorithms. We show that EECA is more efficient than other algorithms. Furthermore, a time distribution (TD) EECA is proposed to handle time-dependent problems, and the time-continuous update mechanism of time-varying pheromone in TD EECA presents a novel idea for combinatorial-continuous problems. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid ac–dc microgrids. A novel two-stage iterative approach is proposed. In the first stage, a meta-heuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and, therefore, can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of ac and dc generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data. [ABSTRACT FROM AUTHOR]

Here, we present the application of an advanced sparse Gaussian process-based machine learning algorithm to the challenge of predicting the yields of inertial confinement fusion (ICF) experiments. The algorithm is used to investigate the parameter space of an extremely robust ICF design for the National Ignition Facility, the “Simplest Design”; deuterium–tritium gas in a plastic ablator with a Gaussian, Planckian drive. In particular, we show that: 1) GPz has the potential to decompose uncertainty on predictions into uncertainty from lack of data and shot-to-shot variation; 2) it permits the incorporation of science-goal-specific cost-sensitive learning (CSL), e.g., focusing on the high-yield parts of parameter space; and 3) it is very fast and effective in high dimensions. [ABSTRACT FROM AUTHOR]

This paper presents fuzzy ${c}$ -means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the genetic algorithm (GA). MC analysis is the most general and reliable technique for yield estimation, yet the considerable amount of time it requires has discouraged its adoption in population-based optimization tools. The proposed methodology reduces the total number of MC simulations that are required, since, at each GA generation, the population is clustered using a fuzzy ${c}$ -means (FCMs) technique, and, only the representative individual (RI) from each cluster is subject to MC simulations. This paper shows that the yield for the rest of the population can be estimated based on the membership degree of FCM and RIs yield values alone. This new method was applied on two real circuit-sizing optimization problems and the obtained results were compared to the exhaustive approach, where all individuals of the population are subject to MC analysis. The FCM approach presents a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. Moreover, a ${k}$ -means-based clustering algorithm was also tested and compared with the proposed FUZYE, with the latest showing an improvement up to 13% in yield estimation accuracy. [ABSTRACT FROM AUTHOR]

To meet the rising demand of mobile data, IEEE 802.11ac was recently developed to offer better network performance than previous IEEE 802.11 standards, representing the 5G Wi-Fi technology. One of the new technologies added to IEEE 802.11ac is a dynamic bandwidth channel access scheme that supports channel bonding (CB) with an up to 160 MHz bandwidth. However, the use of CB increases the number of stations sharing the same medium, resulting in an increased collision rate and severe interference caused by overlapping sub-channels. This problem is further aggravated in densely deployed wireless networks where the service ranges of access points (APs) are likely to be highly overlapping. To resolve these issues, we present an analytical throughput model that considers the effect of both collisions and interference. With this analytical model, we formulate the CB problem to optimize the throughput along with the traffic demand and propose a genetic algorithm for solving the problem. Further, a dynamic channel bonding algorithm (named DCB) is proposed to solve the problem with lower complexity. Extensive simulation results demonstrate that our proposed algorithm provides a higher performance than other recent dynamic CB algorithms with respect to the throughput achieved for the traffic demand. [ABSTRACT FROM AUTHOR]

In order to obtain a good optimization method for the electrical transformer design with optimal selection of parameters, performance evaluation of three evolutionary algorithms (EAs), namely, genetic algorithm (GA), differential evolution algorithm, and nondominated sorting GA (NSGA-II), is carried out. The aim of this paper is to optimize parameters of transformer design (core thicknesses, primary-turn number, secondary-turn number, primary conductor area, and secondary conductor area) for minimization of total power losses (no-load losses and load losses) in three-phase transformer topology while maintaining high efficiency and low cost. The method used for this optimization scheme combines the finite-element method (FEM) and EAs to provide an accurate selection of parameters together with the optimized magnetic flux density and decreased loss. Experimental results show that NSGA-II+FEM model successfully provides a global feasible solution by minimizing total loss and related cost while improving the efficiency of three-phase transformer, rendering it suitable for application in the design environment of industrial transformers. [ABSTRACT FROM AUTHOR]

Fast simultaneous localization and mapping (FastSLAM) is a well-known study for robot navigation. To enhance the performance of FastSLAM, an improved importance sampling is proposed in this paper based on the transformed unscented Kalman filter. The improvement is mainly composed of a novel fuzzy noise estimator, which can adjust the state and observation noises online according to the residual and related covariance, and thus mitigating the defects caused by model inaccuracy. In general, the FastSLAM algorithm suffers from the impoverishment problem since it is essentially a particle filter. Inspired by genetic optimization, an adaptive genetic resampling is proposed to substitute the conventional resampling step to overcome these defects. The proposed method, referred to as the improved transformed unscented FastSLAM, is compared with the unscented FastSLAM and the transformed unscented FastSLAM. The superiorities of the proposed method are verified by simulation and experiment under benchmark environments. [ABSTRACT FROM AUTHOR]

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer. [ABSTRACT FROM AUTHOR]

The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC. [ABSTRACT FROM AUTHOR]

This letter proposes a fast and precise high-speed channel modeling and optimization technique based on machine learning algorithms. Resistance, inductance, conductance, and capacitance (RLGC) matrices of a high-speed channel are precisely modeled by design-of-experiment method and artificial neural network. In addition, an optimal channel design, which achieves minimum channel loss and crosstalk, is investigated within short time by a genetic algorithm. The performance of the proposed technique is validated by simulations up to 20 GHz. [ABSTRACT FROM AUTHOR]

Homography has wide applications in aerial photographic surveys, camera calibration, traffic scene analysis, and sports science, such as player and team performance evaluation. Unlike the mainstream homography that utilizes points as matching features, homography estimation for sports and traffic video can achieve higher accuracy and speed by utilizing straight lines in the scenes, which convey more information than points. Owing to the more stringent requirement of accuracy and computational speed for advanced video analysis, this paper presents a novel homography computational algorithm. Three major novelties are proposed and validated, which are multiple points Hough transform for straight line extraction, correspondence initialization by angle to estimate a set of quasi-optimal solutions, and the feature correspondences optimization to achieve a minimized error using genetic algorithm. With these contributions, the experiments have shown that the proposed algorithm can improve the homography computational accuracy by up to 130% and reduce the processing time by up to 96% over the state-of-the-art algorithms for the same purposes. [ABSTRACT FROM AUTHOR]

Estimation of induction machine parameters, which are commonly used in efficiency evaluation and control methods, can be effectively achieved by utilizing genetic algorithms (GAs). One of the difficulties of using GAs for efficiency estimation is the determination of variable (parameter) constraints (ranges). This paper focuses on the range determination of the parameters (variables) for GA applications. A wide range of variables can cause unstable outcomes. Hence, it is essential to determine an acceptable range for each variable prior to a GA run to produce stable and repeatable results. In this paper, relationships based on the nameplate information and a large database of tested induction motors provided by Hydro-Québec are proposed to determine reasonable induction motor parameter ranges. The proposed method is applied to three different cases. The results of the three cases are compared to each other and also against the corresponding experimental results, which validate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

A typical objective of data visualization is to generate low-dimensional plots that maximally convey the information within the data. The visualization output should help the user not only identify the local neighborhood structure of individual samples, but also obtain a global view of the relative positioning and separation between cohorts. Here, we propose a novel visualization framework designed to satisfy these needs. By incorporating additional cohort positioning and discriminative constraints into local neighbor preservation models through the use of computed cohort prototypes, effective control over the arrangements and proximities of data cohorts can be obtained. We introduce various embedding and projection algorithms based on objective functions addressing the different visualization requirements. Their underlying models are optimized effectively using matrix manifold procedures to incorporate the problem constraints. Additionally, to facilitate large-scale applications, a matrix decomposition based model is also proposed to accelerate the computation. The improved capabilities of the new methods are demonstrated using various state-of-the-art dimensionality reduction algorithms. We present many qualitative and quantitative comparisons, on both synthetic problems and real-world tasks of complex text and image data, that show notable improvements over existing techniques. [ABSTRACT FROM PUBLISHER]

Analog CMOS integrated circuits (ICs) designs depend typically on designer knowledge and experience, as such problems are multivariate and multiobjective, presenting many combinations of input variables to be investigated in order to meet the required specifications. Nowadays, the a posteriori approach is widely used to perform the optimization processes of analog CMOS ICs using evolutionary algorithms. However, these techniques are not totally able to explore potential solutions in specific regions of the Pareto front. Designers then have difficulty in choosing the best solution capable of achieving all desired specifications simultaneously among all the ones found. In this context, the a priori approach using fitness functions has become an important alternative method to overcome these issues of the a posteriori methodology. This paper aims to compare different fitness function profiles used in the a priori optimization processes to boost the effectiveness of the search processes in relation to robustness, accuracy, and yield in analog CMOS ICs designs. We show that the Gaussian profile, proposed here, applied to the lower limit, center value, and upper limit fitness functions is able to improve all the a priori optimization evolutionary techniques investigated, including the genetic, imperialist competitive, and shuffled frog leaping algorithms. [ABSTRACT FROM PUBLISHER]

Multi-installment scheduling (MIS) has shown great effectiveness in minimizing the processing time for large-scale partitionable workloads. To derive an optimal MIS strategy, one has to explicitly determine optimal numbers of installments and processors. Existing studies tend to solve this problem by treating the influence of number of installments (and processors) w.r.t processing time as time-continuous functions and taking the derivative of these functions to determine the optimal values, which may lead to invalid solutions. In this paper, we employ periodic multi-installment scheduling (P-MIS) models for homogeneous and heterogeneous single-level tree networks. Using these models we make the following significant contributions. First, we derive a closed-form solution for an optimal number of installments based on a given network size and a fixed load distribution sequence. Second, we propose a heuristic algorithm for determining an optimal number of processors by first proving several important intermediate lemmas and theorems. Third, for heterogeneous systems, we propose a genetic algorithm to determine an optimal load distribution sequence. Finally, we conduct various experiments to illustrate the effectiveness of the proposed algorithms and perform rigorous analysis on the influence of load distribution sequence on processing time, on the basis of which a practical advice for determining a near-optimal load distribution sequence is given. [ABSTRACT FROM PUBLISHER]

Coriolis flowmeters are well established for the mass flow measurement of single-phase flow with high accuracy. In recent years, attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated into Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 to 14500 kg/h and a gas volume fraction between 0% and 30%. Artificial neural network (ANN), support vector machine (SVM), and genetic programming (GP) models are established through training with the experimental data. The performance of backpropagation-ANN (BP-ANN), radial basis function-ANN (RBF-ANN), SVM, and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN, and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline, while 96.17% of the results are within ±1% on the vertical installation. The SVM models predict the gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on the horizontal and vertical installations, respectively. [ABSTRACT FROM PUBLISHER]

Near-field scanning with phase measurement is always a challenge in practical experiments because of its complexity and lack of accuracy. Therefore, utilizing the magnitude-only information to achieve phase-retrieval and emissions source reconstruction is preferred. This paper proposes a fast and accurate algorithm that can model the emissions from ICs by generating a set of equivalent dipole elements. The process of this algorithm only requires the information of the field magnitudes on two near-field scanning planes of different heights. Unlike other conventional source reconstruction techniques (such as genetic algorithm, gradient optimization), the proposed approach iteratively performs the back-and-forth transformations among the fields and equivalent dipole elements. By optimizing the locations of the equivalent dipole array and the initial values of these dipole elements, the iteration process is capable of quickly converging to the correct electromagnetic field values, including both magnitude and phase. The fields recovered from the calculated equivalent dipole sources have been validated by comparing to the fields emitted from a practical IC source. [ABSTRACT FROM PUBLISHER]

Members in a vendor managed inventory (VMI) supply chain make joint decisions on inventory policy and cooperative advertizing by capitalizing on their interactions. However, very few investigations have been reported to develop methods to facilitate such joint decision making due to the modeling difficulty and computation complexity. This study is, thus, to address the joint VMI, cooperative advertizing, and profit-sharing decision making in a coordinative way. It considers a two-level VMI supply chain including a manufacturer and m retailers, and deals with many decisions, e.g., chain members’ advertizing investments profit sharing. A nonlinear mixed integer Nash bargaining model is developed to model the complex joint decision making of (m +1) players. In view of the difficulties in model solving, this study further develops a solution methodology, including an integrated model and hybrid algorithm for obtaining optimal solutions. Thanks to the integrated model, the hybrid algorithm, which is developed based on analytical methods, a genetic algorithm, and a Lagrange multiplier method, obtains optimal solutions to the Nash bargaining model while greatly reducing computation complexity. Numerical examples demonstrate the validity of the Nash bargaining model and the effectiveness of the solution methodology. Finally, a number of managerial implications are drawn based on sensitivity analysis. [ABSTRACT FROM PUBLISHER]

This paper focuses on the multiobjective solutions of the flow shop scheduling problem with and without sequence dependent setup times. In our case, the two objectives are the minimization of makespan and total flowtime. These problems are solved with a novel multiobjective local search framework-based decomposition, called multiobjective local search based decomposition (MOLSD), which decomposes a multiobjective problem into a number of single objective optimization subproblems using aggregation method and optimizes them simultaneously. First, a problem-specific Nawaz–Enscore–Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a Pareto local search embedded with a heavy perturbation operator is applied to search the promising neighbors of each nondominated solution found so far. Then, when solving each subproblem, a single insert-based local search, a multiple local search strategy, and a doubling perturbation mechanism are designed to exploit the new individual. Finally, a restarted method is used to avoid the algorithm trapping into the local optima, which has a significant effect on the performance of the MOLSD. Comprehensive experiments have been conducted by two standard multiobjective metrics: 1) hyper-volume indicator; and 2) set coverage. The experimental results show that the proposed MOLSD provides better solutions that several state of the art algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. [ABSTRACT FROM PUBLISHER]

Binary sequences with low aperiodic autocorrelation levels, defined in terms of the peak sidelobe level (PSL) and/or merit factor, have many important engineering applications, such as radars, sonars, spread spectrum communications, system identification, and cryptography. Searching for low autocorrelation binary sequences (LABS) is a notorious combinatorial problem, and has been chosen to form a benchmark test for constraint solvers. Due to its prohibitively high complexity, an exhaustive search solution is impractical, except for relatively short lengths. Many suboptimal algorithms have been introduced to extend the LABS search for lengths of up to a few hundred. In this paper, we address the challenge of discovering even longer LABS by proposing an evolutionary algorithm (EA) with a new combination of several features, borrowed from genetic algorithms, evolutionary strategies (ES), and memetic algorithms. The proposed algorithm can efficiently discover long LABS of lengths up to several thousand. Record-breaking minimum peak sidelobe results of many lengths up to 4096 have been tabulated for benchmarking purposes. In addition, our algorithm design can be easily adapted to tackle various extensions of the LABS problem, say, with a generic sidelobe criterion and/or for possibly nonbinary sequences. [ABSTRACT FROM AUTHOR]