Emotion recognition from EEG signals is essential for affective computing and has been widely explored using deep learning. While recent deep learning approaches have achieved strong performance on single EEG emotion datasets, their generalization across datasets remains limited due to the heterogeneity in annotation schemes and data formats. Existing models typically require dataset-specific architectures tailored to input structure and lack semantic alignment across diverse emotion labels. To address these challenges, we propose EMOD: A Unified EEG Emotion Representation Framework Leveraging Valence–Arousal (V–A) Guided Contrastive Learning. EMOD learns transferable and emotion-aware representations from heterogeneous datasets by bridging both semantic and structural gaps. Specifically, we project discrete and continuous emotion labels into a unified V–A space and formulate a soft-weighted supervised contrastive loss that encourages emotionally similar samples to cluster in the latent space. To accommodate variable EEG formats, EMOD employs a flexible backbone comprising a Triple-Domain Encoder followed by a Spatial-Temporal Transformer, enabling robust extraction and integration of temporal, spectral, and spatial features. We pretrain EMOD on 8 public EEG datasets and evaluate its performance on three benchmark datasets. Experimental results show that EMOD achieves the state-of-the-art performance, demonstrating strong adaptability and generalization across diverse EEG-based emotion recognition scenarios.

HCI research increasingly engages with relational notions of ethics, such as care, felt and transcorporeal ethics, as new paradigms for design. Here, we discuss these relational notions in the context of multispecies design, extending it to incorporate Acampora’s concept of Corporal Compassion, which grounds ethics on a shared sense of livingness and vulnerabilities across beings. In order to elicit corporal compassion towards microorganisms, we designed a dining-theatre intervention, which invited participants to ingest and assign moral values to dishes prepared with speculative human-microbial hybrid cells originated from beings exposed to similar levels of bodily distress. Tried with 47 participants in 7 sessions, the work surfaced the idiosyncrasies behind participants’ moral frameworks, and how socialised prescriptive frameworks of ethics sometimes acted as closure towards relational approaches. We discuss how HCI could rethink traditional frameworks of ethics in ways that support relationality, while calling for researchers to make space for moral hesitation.

Precision agriculture demands continuous and accurate monitoring of soil moisture M and key macronutrients, including nitrogen N, phosphorus P, and potassium K, to optimize yields and conserve resources. Wireless soil sensing has been explored to measure these four components; however, current solutions require recalibration (i.e., re-train the data processing model) to handle variations in soil texture (characterized by aluminosilicates Al and organic carbon C, limiting their practicality. To address this, we introduce SoilX, a calibration-free soil sensing system that jointly measures six key components: {M, N, P, K, C, Al}. By explicitly modeling C and Al, SoilX eliminates texture- and carbon-dependent recalibration. SoilX incorporates Contrastive Cross-Component Learning (3CL), with two customized terms: the Orthogonality Regularizer and Separation Loss, to effectively disentangle cross-component interference. Additionally, we design a novel Tetrahedral Antenna Array with an antenna-switching mechanism, which can robustly measure soil dielectric permittivity independent of device placement. Extensive experiments demonstrate that SoilX reduces estimation errors by 23.8% to 31.5% over baselines and generalizes well to unseen fields.

Efforts to integrate living organisms in the design of new technolo- gies are often motivated by prospects of greater sustainability and increased connection with more-than-human worlds. In this paper, we critically discuss these motivations by analysing the vast and mostly hidden ecologies of more-than-human organisms implicated in a biodesign lab experiment. Through the lenses of labour theory, we investigate the extent to which organisms’ bodily functions and relationships can be subsumed into capitalist modes of production. In order to help reveal and map out the network of more-than-human contributors to biodesign, we develop a workshop method and a labour provenance analytical framework that identifies five types of more-than-human labourers, stretching from the centre to the periphery of biodesign. We conclude by discussing how sustainable approaches should account for wider more-than-human ecologies, and how the labour lens could help stress conflicting goals, implicit anthropocentric agendas and ways of improving organismal welfare in biological design and HCI.

Soil composition sensing is essential for precision agriculture, sustainable land management, and optimizing crop yields. However, existing sensing systems face major limitations, including extensive calibration needs to account for soil variability, a narrow focus on measurements for specific properties, and sensitivity to the placement of the device. These challenges hinder practical deployment. This demo shows SoilX, a comprehensive wireless soil sensing system that quantifies all major soil components---including aluminosilicates, water, organic carbon, and micronutrients---using RF and VNIR sensing technologies. To enable the generalizability, SoilX employs contrastive pretraining to mitigate cross-component interference. Additionally, a tetrahedron-based antenna geometry ensures robustness to device placements. Extensive evaluations in both lab and field settings demonstrate that SoilX achieves state-of-the-art accuracy in soil composition analysis with low costs.

We address a fundamental challenge in coordinating large-scale 3D drone swarms: how to achieve rapid collective response to environmental stimuli while ensuring group stability and safety. Existing swarm navigation modals often rely on sophisticated individual perception and communication capabilities, which can be computationally expensive and impractical for large swarms. In this paper, we propose the Non-reciprocal Collective Emergent Navigation model (NRCE), a decentralized approach designed for real-world drone flocking in complex environments. Unlike traditional models, our approach leverages localized non-reciprocal interactions, where boundary drones detect environmental stimuli and propagate this information throughout the swarm without directly controlling individual trajectories. Through extensive numerical simulations and physical experiments with up to 28 drones, we demonstrate how this model achieves coordinated collective motion while effectively balancing stability with responsiveness. Our findings reveal two notable insights: (1) intermediate cohesion levels (ωc) optimize collective response—a "Goldilocks zone" where individuals are neither too tightly coupled nor too independent, challenging the conventional wisdom that stronger cohesion always improves coordination; and (2) swarm queue configuration significantly affects optimal interaction parameters, with divergent trends observed between attraction- and repulsion-based coordination mechanisms as layer count increases. These discoveries provide critical design principles for cost-effective, high-density swarm systems while advancing the theoretical understanding of collective dynamics in both artificial and biological systems.

Ant Colony Optimization (ACO) is a prominent swarm intelligence algorithm extensively applied to path planning. However, traditional ACO methods often exhibit shortcomings, such as blind search behavior and slow convergence within complex environments. To address these challenges, this paper proposes the Pheromone-Focused Ant Colony Optimization (PFACO) algorithm, which introduces three key strategies to enhance the problem-solving ability of the ant colony. First, the initial pheromone distribution is concentrated in more promising regions based on the Euclidean distances of nodes to the start and end points, balancing the trade-off between exploration and exploitation. Second, promising solutions are reinforced during colony iterations to intensify pheromone deposition along high-quality paths, accelerating convergence while maintaining solution diversity. Third, a forward-looking mechanism is implemented to penalize redundant path turns, promoting smoother and more efficient solutions. These strategies collectively produce the focused pheromones to guide the ant colony’s search, which enhances the global optimization capabilities of the PFACO algorithm, significantly improving convergence speed and solution quality across diverse optimization problems. The experimental results demonstrate that PFACO consistently outperforms comparative ACO algorithms in terms of convergence speed and solution quality.

Efficient feature learning is considered indispensable for maximizing the representation of scarce information in tiny datasets. However, existing methods are often unable to fully exploit local features and contextual dependencies when dealing with tiny datasets. To overcome this shortcoming, a Cross-Feature Vision Transformer (CF-ViT) was proposed, which decouples local feature refinement from global context modeling and leverages the complementary strengths of CNNs and Transformers. Specifically, a Cross-Scale Fusion (CSF) module was introduced to integrate features from multiple scales, ensuring that cross-scale information is globally embedded. In addition, a Feature Enhancement and Reorganization (FER) module was incorporated into CF-ViT, whereby Transformer outputs are reorganized into 2D feature maps for convolution-based detail enhancement to thoroughly exploit local information. Extensive experiments have demonstrated that CF-ViT consistently surpasses baselines across 4 tiny datasets, reaching a 96.87% (KSDD) Top-1 accuracy with only 29.19 million parameters and 2.67 billion FLOPs. Moreover, a Top-1 accuracy of 85.03% is attained on a real-world tiny dataset of wood surface defect detection, exceeding all baselines. These findings underscore the effectiveness and generalization capability of CF-ViT in capturing fine-grained local details and global context, offering a promising and deployable solution for vision tasks in tiny datasets.

Wireless capsule endoscopy is a non-invasive and painless approach for diagnosing gastrointestinal diseases. An automated medical decision support system can significantly enhance clinician efficiency and reduce the incidence of mis-diagnosis when analyzing lesions within the 50,000 to 100,000 image frames generated for each individual. However, only a small number of previous studies have focused on fine-grained recognition characteristics, and very few have simultaneously addressed both fine-grained recognition and class imbalance issues in wireless capsule endoscopy. To address these issues, we propose a novel medical decision support network that includes a prototype attention enhancement module and a dynamic curriculum learning approach. The prototype attention enhancement module improves lesion-sensitive compact representation learning by leveraging cosine similarity between the representation and a prototype memory with multi-class centers. The dynamic curriculum learning adopts triplet loss and weighted cross-entropy, facilitated by a progressive factor that controls between fine-grained representation learning and class-sensitive learning. Through extensive comparative experiments on three public datasets, the proposed network demonstrated competitive performance, outperforming previous methods with an F1-score of 96.6% on the 10-class Kvasir-Capsule dataset, an accuracy of 98.3% on the CAD-CAP dataset, and an accuracy of 93.7% on the mixed KID dataset. Two additional gastrointestinal histopathology datasets confirm the generalization of the proposed network. The code will be available at https://github.com/Xingcun-Li/MDSN-WCE.

In logistics systems with multiagent collaboration, one of the prevailing focus lies on modeling as the dynamic multiperiod vehicle routing problem (DMPVRP). This work introduces modifications to DMPVRP to align with the requirements of real factory operations, particularly with dynamic supply demands. A self-established multiagent dynamic scheduling framework has been proposed to adapt to dynamic environmental changes and make timely adjustments, which consists of two modules: dynamic path planning and machine assignment. The first module utilizes a self-designed multioperator two-stage evolutionary algorithm to dynamically update the routes for vehicles. The second module maintains the workload balance among vehicles in real time. Experimental results demonstrate that the proposed algorithm achieves optimal outcomes compared to three state-of-the-art algorithms, surpassing others by 20% in machine output and exhibiting 5% lower transportation costs. In addition, a case study from a steel cord manufacturing factory is conducted, demonstrating its capability to promptly enhance efficiency.

In the face of a sudden public health emergency caused by a new infectious disease, it is necessary to establish a multi-level emergency medical facility (including primary and superior facilities) to address the surge in medical needs. In this context, traditional hospitals are responsible for patient screening, primary emergency medical facilities are responsible for treating mild cases, and superior emergency medical facilities are responsible for treating severe cases. Against the backdrop of uncertainties such as patient self-referral and the autonomous progression of the disease, we address an important problem of integrated emergency medical facility location and patient dispatching under uncertainty and propose a multi-stage stochastic programming model to formulate the problem. For a deterministic model under a given set of scenarios, a Decomposition-based Dual-level Heuristic (DDH) algorithm is proposed to efficiently solve the problem, where the upper level employs tabu search to optimize the location scheme, and the lower level utilizes a patient allocation heuristic to provide an optimized patient dispatching solution. Numerical experiments are conducted using Wuhan, China, the epicenter of the COVID-19 outbreak, as an example. The results show that the DDH algorithm achieves high quality solutions close to those obtained by state-of-the-art solver CPLEX but with significantly reduced computational overload. The DDH algorithm is also compared with the progressive hedging algorithm and genetic algorithm, showing its superior performance in terms of solution quality and computational efficiency. Through extensive data analysis, valuable conclusions and managerial insights are obtained, providing useful references for emergency response in similar public health emergencies in the future.

Precise robot localization at the tree level is essential for smart agriculture applications such as precision disease management and targeted nutrient distribution. Existing methods fail to achieve the required accuracy. We propose OrchLoc, a fingerprinting-based localization solution that achieves treelevel precision using a single Long Range (LoRa) gateway. Our approach utilizes channel state information (CSI) across eight channels as a localization fingerprint. To minimize labor-intensive site surveys for fingerprint database construction and maintenance, we develop a CSI generative model (CGM) that learns the relationship between CSI vectors and their corresponding locations. The CGM is fine-tuned using CSI data from static agricultural LoRa sensor nodes, enabling continuous fingerprint database updates. Extensive experiments in two orchards demonstrate that OrchLoc effectively achieves accurate tree-level localization with minimal overhead, improving robot navigation

LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g., determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for orchard environments, because they do not consider the shadowing effect on wireless propagation caused by the ground and tree canopies. This article presents FLog , a propagation model for LoRa signals in orchard environments. FLog leverages a unique feature of orchards, i.e., all trees have similar shapes and are planted regularly in space. We develop a three-dimensional model of orchards. Once we have the location of a sensor and a gateway, we know the media that the wireless signal traverses. Based on this knowledge, we generate the First Fresnel Zone (FFZ) between the sender and the receiver. The intrinsic path loss exponents of all media can be combined into a classic Log-Normal Shadowing model in the FFZ. Extensive experiments in almond orchards show that FLog reduces the link quality estimation error by 42.7% and improves gateway coverage estimation accuracy by 70.3%, compared with a widely used propagation model. The source codes and dataset are released at https://github.com/ycucm/Flog .

Recent work in HCI has called for deeper ethical considerations when engaging with more-than-human organisms in design. In this paper, we introduce Microbial Revolt, a provocative method to support reflection on the perspectives of organisms involved in HCI and design practice. By asking participants to consider the reality of a chosen organism in feral and lab environments and to redesign lab tools in order to account for their “non-participation”, we identified the manifestation of key epistemic differences between approaches to care and ecologies in typical design and biology research - as well as the potential for design and HCI to creatively redefine power dynamics in the lab. Further interviews revealed specific challenges and opportunities that designers and HCI researchers face in adapting practices to lab standards, and lab equipment to their practices, calling for a redefinition of tools, spaces and guidance to accommodate phenomenological perspectives and multiple modes of interaction with living organisms.

The rapid decline in groundwater around the world poses a significant challenge to sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by artificially flooding agricultural lands using surface water. Ag-MAR requires a carefully selected flooding schedule to avoid affecting the oxygen absorption of crop roots. However, current Ag-MAR scheduling does not take into account complex environmental factors such as weather and soil oxygen, resulting in crop damage and insufficient recharging amounts. This paper proposes MARLP, the first end-to-end data-driven control system for Ag-MAR. We first formulate Ag-MAR as an optimization problem. To that end, we analyze four-year in-field datasets, which reveal the multi-periodicity feature of the soil oxygen level trends and the opportunity to use external weather forecasts and flooding proposals as exogenous clues for soil oxygen prediction. Then, we design a two-stage forecasting framework. In the first stage, it extracts both the cross-variate dependency and the periodic patterns from historical data to conduct preliminary forecasting. In the second stage, it uses weather-soil and flooding-soil causality to facilitate an accurate prediction of soil oxygen levels. Finally, we conduct model predictive control (MPC) for Ag-MAR flooding. To address the challenge of large action spaces, we devise a heuristic planning module to reduce the number of flooding proposals to enable the search for optimal solutions. Real-world experiments show that MARLP reduces the oxygen deficit ratio by 86.8% while improving the recharging amount in unit time by 35.8%, compared with the previous four years.

In orchards, tree-level localization of robots is critical for smart agriculture applications like precision disease management and targeted nutrient dispensing. However, prior solutions cannot provide adequate accuracy. We develop our system, a fingerprinting-based localization system that can provide tree-level accuracy with only one LoRa gateway. We extract channel state information (CSI) measured over eight channels as the fingerprint. To avoid labor-intensive site surveys for building and updating the fingerprint database, we design a CSI Generative Model (CGM) that learns the relationship between CSIs and their corresponding locations. The CGM is fine-tuned using CSIs from static LoRa sensor nodes to build and update the fingerprint database. Extensive experiments in two orchards validate our system's effectiveness in achieving tree-level localization with minimal overhead and enhancing robot navigation accuracy.

The quadratic unconstrained binary optimization (QUBO) is a well-known NP-hard problem that takes an $n\times n$ matrix $Q$ as input and decides an $n$-dimensional 0-1 vector $x$, to optimize a quadratic function. Existing learning-based models that always formulate the solution process as sequential decisions suffer from high computational overload. To overcome this issue, we propose a neural solver called the Value Classification Model (VCM) that formulates the solution process from a classification perspective. It applies a Depth Value Network (DVN) based on graph convolution that exploits the symmetry property in $Q$ to auto-grasp value features. These features are then fed into a Value Classification Network (VCN) which directly generates classification solutions. Trained by a highly efficient model-tailored Greedy-guided Self Trainer (GST) which does not require any priori optimal labels, VCM significantly outperforms competitors in both computational efficiency and solution quality with a remarkable generalization ability. It can achieve near-optimal solutions in milliseconds with an average optimality gap of just 0.362\% on benchmarks with up to 2500 variables. Notably, a VCM trained at a specific DVN depth can steadily find better solutions by simply extending the testing depth, which narrows the gap to 0.034\% on benchmarks. To our knowledge, this is the first learning-based model to reach such a performance.

For a simple undirected weighted graph G=(V,E,w,c), the weighted total domination problem is to find a total dominating set S with the minimum weight cost. A total dominating set S is a vertex subset satisfying that for each vertex in V there is at least one neighboring vertex in S. We propose a knowledge-based iterated local search algorithm for this problem that combines a reduction procedure to reduce the input graph, a learning-based initialization to generate high-quality initial solutions and a solution-based iterated local search to conduct intensive solution examination. Experiments on 342 benchmark instances show that the algorithm outperforms state-of-the-art algorithms. In particular, it reports 93 new upper bounds and 249 same results (including 165 known optimal results). The impact of each component of the algorithm is examined.

The capacitated electric vehicle routing problem (CEVRP) extends the traditional vehicle routing problem by simultaneously considering the service order of the customers and the recharging schedules of the vehicles. Due to its NP-hard nature, we decompose the original problem into two sub-problems: a capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). A highly effective threshold acceptance based multi-layer search (TAMLS) algorithm is proposed to quickly obtain high-quality solutions. TAMLS consists of three layers. An iterated thresholding search procedure and a thresholding selection procedure are employed to produce diversified CVRP solutions in the first layer and to screen out high quality ones in the second layer, respectively. In the third layer, a removal heuristic coupling with an enumeration method is adopted to solve FRVRP, which produces optimized charging schedules. Extensive computational results show that TAMLS outperforms the state-of-the-art algorithms in terms of both solution quality and computation time. In particular, it is able to obtain new best results for 11 out of 17 benchmark instances, and reach the best known results on the remaining 6 instances. Additional experimental analyses are performed to better understand the contributions of key algorithmic components.

The agile earth observation satellite scheduling problem (AEOSSP) with time-dependent transition times is a complex combinational optimization problem that has emerged from the development of large-scale satellite management techniques. To address this problem, we propose a deep reinforcement learning-based construction model (DRL-CM) that consists of five parts: 1) a Markov decision process (MDP); 2) a feature engineering; 3) a constructive heuristic neural network (CHNN); 4) an RL training method; and 5) an evaluation system. Specifically, the CHNN comprises six modules containing three special components that we propose: a dynamic encoder, a dynamic global layer, and a two-stage attention layer. First, we build the MDP of the AEOSSP and the feature engineering with effective features required for decision-making. Second, we design the CHNN to function as the MDP policy and train it with an RL model. Finally, we propose a comprehensive evaluation system for the validation of our model. The experimental results indicate that the proposed DRL-CM outperforms the state-of-the-art algorithm in terms of both optimization speed and quality. In addition, the feature engineering and network architecture built in our model are verified to be effective in comprehensive experiments.

LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g., determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for orchard environments, because they do not consider the shadowing effect on wireless propagation caused by the ground and tree canopies. This paper presents FLog, a propagation model for LoRa signals in orchard environments. FLog leverages a unique feature of orchards, i.e., all trees have similar shapes and are planted regularly in space. We develop a 3D model of the orchards. Once we have the location of a sensor and a gateway, we know the mediums that the wireless signal traverse. Based on this knowledge, we generate the First Fresnel Zone (FFZ) between the sender and the receiver. The intrinsic path loss exponents (PLE) of all mediums can be combined into a classic Log-Normal Shadowing model in the FFZ. Extensive experiments in almond orchards show that FLog reduces the link quality estimation error by 42.7% and improves gateway coverage estimation accuracy by 70.3%, compared with a widely-used propagation model.

This paper examines fault resilience among citizen-science air quality monitoring networks in California's economically challenged San Joaquin Valley (SJV). We examine disparities in monitoring capabilities and data equity between the SJV and the San Francisco Bay Area. We found significant inequities through experimental analysis simulating sensor failures. Our results emphasize the need for reliable monitoring systems and advanced modeling algorithms in resource-limited areas.

Satellite Imaging Scheduling, Satellite Downlinking Scheduling and Ground Resources Scheduling are important components in satellites daily management. Considering the highly interlinking of these three types of scheduling, an Integrated Satellite Scheduling model is formulated and proved NP-complete in this paper. To address the large scale and oversubscription of the Integrated Satellite Scheduling in an actual background, an evolvable real-time system of Integrated Satellite Scheduling is constructed based on Cooperative Neuro Evolution of Augmenting Topologies (C-NEAT). With the help of the C-NEAT, the system learns from historical scheduling data and adaptively assigns each request to the satellite or the ground antenna which is most likely to fulfill this request. Moreover, the real-time scheduling function of the system is actualized by the windowed scheduling framework. Experimental results indicate that the system greatly reduces the problem size of Integrated Satellite Scheduling and improves the scheduling efficiency, where daily and emergent requests are arranged over time.

Agile earth observing satellites(AEOS) scheduling problem has drawn much research attention in the last few years due to its wide range of applications in both military and civilian areas. Recently, a state-of-the-art adaptive large neighborhood search algorithm(ALNS) was proposed for solving this problem where a dichotomy algorithm was used to calculate the time-dependent transition time. ALNS was demonstrated to be very effective in comparison to CPLEX and other heuristic algorithms at the time of publishment. However, we find two major drawbacks of ALNS. First, the imbalance between diversification and intensification leads to premature convergence; Second, the frequent invoke of the dichotomy algorithm leads to tremendous duplicate calculations and thus high computational complexity of ALNS. In this paper, we propose a simple and fast heuristic algorithm (SFHA) which is composed of an intensified local improvement procedure and a diversified perturbation operation. SFHA also includes a preprocessing procedure to precalculate all possible transition times, which avoids duplicate calculation and speeds up search process. Computational results show that the proposed SFHA outperforms ALNS in terms of both solution quality and computational time. In particular, SFHA consistently attains better solutions than ALNS in half of its running time.

This paper presents a multi-satellite scheduling problem for very large area observation in response to various requests. It is assumed that the profit was proportional to the coverage of the acquired area, and the objective is therefore to maximize the total profits of generated observation schedule. To address the satellite scheduling problem, we first demonstrate a detailed problem description and then transform the problem into set covering problem within several criteria and constraints. Based on that, a mathematical model is established. To solve the problem, a new three-phase solving framework is proposed. In the discretizing phase, an area discretization method is adopted to establish the evaluation system. In the target decomposing phase, area target is decomposed into strips and corresponding visible time windows are calculated. In the scheduling phase, with crossover, mutation and feasibility operators, a genetic algorithm is introduced to generate an optimal observation schedule. Through extensive computational experiments on realistically generated problems in various scenarios including real-world data from China's satellite platform, the effectiveness and reliability of the proposed solving framework are verified.

The active-scan agile earth observation satellite (AS-AEOS) is highly agile in three axis which enables in-motion imaging, allowing any imaging direction for a given ground target. Such a high agility drastically increases the difficulty of the scheduling problem. In this paper, we consider a bi-objective AS-AEOS scheduling problem such that we simultaneously maximize the total reward and the overall quality of the scheduled requests, seeking to maximize the profit gain of the satellite owners and the satisfaction of the customers. The main contributions of this paper are two folds: the proposal of a constrained optimization model for formulating the problem, and the design of a Hybrid Coding Based Multi-objective Differential Evolution (HCBMDE) method for the problem solution. In computational experiments, two category scenarios are designed to test the effectiveness of the proposed HCBMDE method. Computational results show that, the proposed method is able to achieve a high quality approximate Pareto front.

Agile Earth Observing Satellite (AEOS) scheduling problem (AEOSSP) consists in selecting a subset of tasks from a given task set which are then scheduled on the agile satellite with the purpose of maximizing the total reward of scheduled tasks. AEOSSP is strongly NP-hard and therefore existing solution approaches mainly fall in the field of heuristics and metaheuristics. According to the no free lunch theory, it is impossible to find a single heuristic that is well-applied to any problem instance and a problem-tailored heuristic is always needed. In this paper, we propose a genetic programming based evolutionary approach (GPEA) to automatically evolve a best-suited constructive heuristic for any given AEOSSP instance. The programs (individuals) of GPEA are heuristic rules encoded as trees of mathematical functions. The fitness of the program is evaluated through mapping the mathematical function to an AEOSSP solution using a timeline-based construction algorithm. Computational results on a set of well-designed AEOSSP scenarios show that the proposed GPEA leads to a heuristic algorithm that outperforms recently published sophisticated meta-heuristic algorithm (ALNS). Additional experiments were carried out to demonstrate that the timeline based construction algorithm plays a significant role in matching time-related characteristics in comparison to four commonly used heuristic algorithms. Our results also showed that the evolved heuristic rules preserve a certain extent of generality.

The generalized quadratic multiple knapsack problem (GQMKP) extends the classical quadratic multiple knapsack problem with setups and knapsack preference of the items. The GQMKP can accommodate a number of real-life applications and is computationally difficult. In this paper, we demonstrate the interest of the memetic search approach for approximating the GQMKP by presenting a highly effective memetic algorithm (denoted by MAGQMK). The algorithm combines a backbone-based crossover operator (to generate offspring solutions) and a multineighborhood simulated annealing procedure (to find high quality local optima). To prevent premature convergence of the search, MAGQMK employs a quality-and-distance (QD) pool updating strategy. Extensive experiments on two sets of 96 benchmarks show a remarkable performance of the proposed approach. In particular, it discovers improved best solutions in 53 and matches the best known solutions for 39 other cases. A case study on a pseudo real-life problem demonstrates the efficacy of the proposed approach in practical situations. Additional analyses show the important contribution of the novel general-exchange neighborhood, the backbone-based crossover operator as well as the QD pool updating rule to the performance of the proposed algorithm.