3D object detection is crucial to ensure the reliability and stability of autonomous driving systems. In recent years, researchers have made great progress in 3D object detection by combining features of images and point clouds. However, there is still much room for detection accuracy improvement, especially for small object detection. In this paper, we propose a frustum-based 3D object detection model named PointFPN. The core idea of PointFPN is learning expressive semantic and contextual information for small objects, e.g., pedestrians and cyclists in an urban street scene. To detect 3D objects, our model uses frustums to bridge the gap between images and point clouds and thus generating proposals. Then, a feature pyramid structure is designed to extract and fuse multi-level features of target objects represented by point clouds. Meanwhile, we develop a multilevel regression network to calculate different parameters of 3D bounding boxes at different feature levels. Through elaborate structure designed above, our model can learn discriminative features which are highly relevant to bounding box parameters at different feature levels. Experimental study shows that our model is effective in detecting small objects and has a strong robustness to sparse point clouds. Our model demonstrates state-of-the-art performance on small object detection on KITTI benchmark.

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for the solving of continuous optimization problems. In the last decades, a plethora of DE variants have been proposed in the literature for enhanced optimization performance. However, most of these DE variants are designed to solve a single problem in a single run. Recently, a multifactorial DE (MFDE) has been proposed to conduct evolutionary search on multiple tasks simultaneously. Benefitting from the implicit knowledge transfer among different tasks, MFDE has demonstrated a superior performance against the single-task DE in terms of convergence speed and solution quality. In MFDE, the knowledge transfer is realized via the mutation operation conducted on solutions with different skill factors. However, despite a lot of mutation strategies suggested in the literature, the current MFDE takes DE/rand/1 as the only strategy for knowledge transfer. The impacts of different mutation strategies on the performance of MFDE is still unexplored. Taking this cue, in this paper, we embark a study to investigate how different mutation strategies for knowledge transfer affect the performance of MFDE. In particular, besides DE/rand/1, another four commonly-used mutation strategies are adapted for the purpose of multitask optimization. Further, towards effective mutation for knowledge transfer in MFDE, a new mutation strategy called DE/best/1+ρ, which is able to adjust its behavior along the search process is proposed. Lastly, comprehensive empirical studies are conducted to investigate the performance of existing and the new proposed mutation strategies on the 9 single-objective multitasking benchmarks.

Dynamic multi-objective optimization problem (D-MOP) is widely existed in many real-world applications. Over the years, DMOP has attracted many research attentions in the literature. The adaptive indicator-based evolutionary algorithm (IBEA2) is a recently proposed multi-objective evolutionary algorithm (MOEA). It has demonstrated strong search capability on commonly used multi-objective benchmarks over state-of-the-art MOEAs. However, as the adaptation of parameter$k$is based on the selected solutions with maximum hypervolume, this mechanism will be inappropriate if the problem changes over time. The reason is that the solutions with high hypervolume at one particular time instance may not be with high hypervolume at another if the problem changed. Keeping this in mind, inspired by the recent autoencoding evolutionary search, which is able to transfer the past search experiences to improve the evolutionary search on unseen problems, in this paper, we propose to extend the IBEA2 by adapting k with transferred high hypervolume solutions obtained before the dynamic change occurs, for solving DMOP. To evaluate the proposed method, empirical comparisons on the commonly used Farina-Deb-Amato (FDA) DMOP benchmarks, against both the IBEA2 and one recently proposed dynamic MOEA, are presented.

Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers. Most of the existing VRPPC works consider deter- ministic parameters which may not be practical and uncertainty has to be taken into account. In this paper, we propose the Optimal Stochastic Delivery Planning with Deadline (ODPD) to help a supplier plan and optimize the package delivery. The aim of ODPD is to service all customers within a given deadline while considering the randomness in customer demands and traveling time. We formulate the ODPD as a stochastic integer programming, and use the cardinality minimization approach for calculating the deadline violation probability. To accelerate computation, the L-shaped decomposition method is adopted. We conduct extensive performance evaluation based on real customer locations and traveling time from Google Map.

With an increasing demand from emerging logistics businesses, Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been introduced to manage package delivery services from a supplier to customers. However, almost all of existing studies focus on the deterministic problem that assumes all parameters are known perfectly at the time when the planning and routing decisions are made. In reality, some parameters are random and unknown. Therefore, in this paper, we consider VRPPC with hard time windows and random demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to minimize the total package delivery cost while meeting the customer time window constraints. We use stochastic integer programming to formulate the optimization problem incorporating the customer demand uncertainty. Moreover, we evaluate the performance of the ODP using test data from benchmark dataset and from actual Singapore road map.

To enhance the search performance of evolutionary algorithms, reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and demonstrated much promise. In the literature, there are generally three types of approaches for reusing knowledge from past search experiences, namely exact storage and reuse of past solutions, the reuse of model-based information, and the reuse of structured knowledge captured from past optimized solutions. In this paper, we focus on the third type of knowledge reuse for enhancing evolutionary search. In contrast to existing works, here we focus on knowledge transfer across heterogeneous continuous optimization problems with diverse properties, such as problem dimension, number of objectives, etc., that cannot be handled by existing approaches. In particular, we propose a novel autoencoding evolutionary search paradigm with learning capability across heterogeneous problems. The essential ingredient for learning structured knowledge from search experience in our proposed paradigm is a single layer denoising autoencoder (DA), which is able to build the connections between problem domains by treating past optimized solutions as the corrupted version of the solutions for the newly encountered problem. Further, as the derived DA holds a closed-form solution, the corresponding reusing of knowledge from past search experiences will not bring much additional computational burden on the evolutionary search. To evaluate the proposed search paradigm, comprehensive empirical studies on the complex multiobjective optimization problems are presented, along with a real-world case study from the fiber-reinforced polymer composites manufacturing industry.

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

Satellite data is discrete in both space and time; it can be considered as temporal snapshots (time series) of lattice processes. As the raw datasets are often too large to host publicly, processed datasets with a coarse spatial resolution are often hosted as an alternative. Nevertheless, with a regular grid, the inhomogeneous variability in the lattice processes cannot be captured effectively. In this paper, a quadtree-based spatial data dimension reduction algorithm is demonstrated. Based on the stratum variance, this algorithm iteratively divides lattice data into strata of fours. In this way, the number of strata in an area can be correlated to the variability of that area. A satellite-derived surface solar radiation (SSR) dataset is used for the case study. Using parallel computing, the quadtree algorithm is applied on each temporal snapshot of SSR in the dataset. The processed data is then saved in a list structure. Finally, a solar resource assessment application, namely, optimizing the orientation of a photovoltaic array, is considered to demonstrate the effectiveness and efficiency of the dimension-reduced dataset.

Coordination across a supply chain creates win-win situation for all players in that supply chain; we address the benefits, in terms of forecast accuracy, of reconciling demand forecasts across a supply chain. In Part III of this three-part paper, we continue our discussion on optimal reconciliation of forecasts. Two contributions are made in this paper: 1) the grouped reconciliation technique is used to address the forecast inconsistency in situations when more than one hierarchy can be defined in a supply chain, and 2) minimum trace (MinT) estimator is used to further improve the reconciliation accuracy on top of the weighted least square (WLS) approach, which was used in the earlier parts of this three-part paper. Following the earlier works, the same set of fast moving consumer goods data is used here. The current results are compared to the previous ones. It is shown that the MinT reconciliation technique outperforms the WLS approach, which has been previously identified as the best reconciliation technique for the data from the bottled juice category in the Dominick's Finer Food dataset.

The Robust Vehicle Routing Problem with Time Windows has been gaining popularity over the past few years due to its focus on tackling uncertainty inherent to real world problems. Most of the current approaches in generating robust solutions require prior knowledge on the uncertainties, such as uncertainties in travel time. Hence, they are less than favorable to use in the absence of data, i.e., in the case of data starvation. In this paper, we present an evolutionary algorithm that in the absence of data on travel time uncertainty, provides a decision maker with a collection of solutions, each with a corresponding level of trade-off between total travel distance and solution robustness. In particular, we present a novel realization of route flexibility and its relation to solution robustness. Furthermore, we propose a bi-objective evolutionary algorithm for the vehicle routing problem with time windows where the objectives are (a) total travel distance and (b) solution flexibility. The proposed algorithm is tested on the well-known Solomon benchmarks and a trade-off analysis between total distance and solution flexibility is provided based on the obtained test results. Based on observations from the trade-off analysis, a number of suggestions to improve the current logistics system are provided.

Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator) and the encountered MOPs. In this paper, a new version of IBEA1 (labeled as IBEA2 hereafter) is proposed to adaptively adjust parameter κ for solving various MOPs. The core idea of IBEA2 is to adapt parameter κ for the purpose of selecting the subset of offspring solutions with the maximum hypervolume into the next population. Experimental studies on 44 benchmark MOPs with 2-5 objectives in jMetal verified that IBEA2 is able to find higher hypervolumes against the four classical MOEAs, which are NSGAII, SPEA2, MOEA/D and IBEA1, in the literature.

The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this paper, a new MOEA/D with a fast hypervolume archive (called FV-MOEA/D) is proposed to adaptively adjust the weight vectors for various shapes of PFs. The core idea of FV-MOEA/D is to periodically adjust weight vectors based on solutions in the proposed archive, in which convergence and diversity are maintained by maximizing hypervolume. Experimental studies on 58 benchmark MOPs in jMetal demonstrate that the proposed FV-MOEA/D not only reached higher hypervolumes when compare to five classical MOEAs i.e., NSGAII, SPEA2, IBEA, FV-MOEA and MOEA/D, but also obtained well distributed weight vectors on PFs with different geometrical characteristics.

Collaboration between logistics service providers (LSPs) becomes increasingly crucial in urban last-mile logistics as it not only helps LSPs to reduce the transportation costs and increase the truck load factor but also improve customer service levels. However, LSPs are usually against sharing of customer data and delivery orders with their competitors. This makes most existing collaboration strategies in literature impractical in reality. To deal with this issue, we proposed a new collaboration strategy that requires less coordination efforts between the LSPs involved. In addition, we carried out a numerical experiment with the data collected from the local LSPs in Singapore to illustrate our proposed strategy. The results and the analysis highlighted shows that the proposed collaboration strategy is not only more practical than other conventional strategies, it also helps to reduce the total transportation costs and increase truckload in overall.

In a big data enabled environment, manufacturers and distributors may have access to previously unobserved retailer-level demand related information. This additional information can be considered in demand forecasting to produce more accurate forecasts, and thus enable better stock-outs management. In Part II of this two-part paper, we explore the hierarchical nature of fast moving consumer goods (FMCG) demand (represented by sales) time series and produce one week ahead rolling forecasts on universal product code (UPC) level (or distributor level, as per our definition below). We show that the hierarchical forecasting framework has significant accuracy improvement over the conventional univariate forecasting methods. The main reason of the observed improvements is due to the price and promotion information available at the retailer level, which is assumed to be unknown to the distributor. To reconcile forecasts according to the hierarchy, only the forecast values at retailer level are needed, the business strategies of individual retailers remain proprietary. A freely available dataset is considered to encourage further exploration. Data exploratory analysis and visualization tools are discussed in Part I of the paper.

To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measuring the exact HV contributions of different solutions. To cope with this problem, in this paper, a simple and fast hypervolume indicator-based MOEA (FV-MOEA) is proposed to quickly update the exact HV contributions of different solutions. The core idea of FV-MOEA is that the HV contribution of a solution is only associated with partial solutions rather than the whole solution set. Thus, the time cost of FV-MOEA can be greatly reduced by deleting irrelevant solutions. Experimental studies on 44 benchmark multiobjective optimization problems with 2-5 objectives in platform jMetal demonstrate that FV-MOEA not only reports higher hypervolumes than the five classical MOEAs (nondominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm 2 (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), indicator-based evolutionary algorithm, and S-metric selection based evolutionary multiobjective optimization algorithm (SMS-EMOA)), but also obtains significant speedup compared to other HV indicator-based MOEAs.

Watchdog technique is a fundamental building block to many trust systems that are designed for securing wireless sensor networks (WSNs). Unfortunately, this kind of technique consumes much energy and hence largely limits the lifespan of WSN. Although the state-of-the-art studies have realized the importance of trust systems' efficiency in WSNs and proposed several preliminary solutions, they have overlooked to optimize the watchdog technique, which is perhaps among the top energy-consuming units. In this paper, we reveal the inefficient use of watchdog technique in existing trust systems, and thereby propose a suite of optimization methods to minimize the energy cost of watchdog usage, while keeping the system's security in a sufficient level. Our contributions consist of theoretical analyses and practical algorithms, which can efficiently and effectively schedule the watchdog tasks depending on the sensor nodes' locations and the target nodes' trustworthiness. We have evaluated our algorithms through experiments on top of a WSNET simulation platform and an in-door WSN testbed in our collaborative lab. The results have successfully confirmed that our watchdog optimization techniques can save at least 39.44% energy without sacrificing much security (<;0.06 in terms of trust accuracy and robustness), even in some cases enhance the protection against certain attacks.

An important consideration of multiobjective optimization (MOO) is the quantitative metrics used for defining the optimality of different solution sets, which is also the basic principle for the design and evaluation of MOO algorithms. Although a plethora of performance metrics have been proposed in the MOO context, there has been a lack of insights on the relationships between metrics. In this paper, we first group the major MOO metrics proposed to date according to four core performance criteria considered in the literature, namely, capacity, convergence, diversity, and convergence-diversity. Then, a comprehensive study is conducted to investigate the relationships among representative group metrics, including generational distance, ϵ-indicator (I(1)ϵ+), spread (∆), generalized spread (∆∗), inverted generational distance, and hypervolume. Experimental results indicated that these six metrics show high consistencies when Pareto fronts (PFs) are convex, whereas they show certain contradictions on concave PFs.

In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as “expensive” in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an “expensive” objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the “expensive” objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.