Evolve ensemble rules automatically for the block spatial scheduling under dynamic environments via surrogate-assisted cooperative evolution genetic programming

Purpose With the increasing demand for surveillance and smart transportation, drone technology has become the center of attraction for robotics researchers. This study aims to introduce a new path planning approach to drone navigation based on topology in an uncertain environment. The main objective of this study is to use the Ricci flow evolution equation of metric and curvature tensor over angular Riemannian metric, and manifold for achieving navigational goals such as path length optimization at the minimum required time, collision-free obstacle avoidance in static and dynamic environments and reaching to the static and dynamic goals. The proposed navigational controller performs linearly and nonlinearly both with reduced error-based objective function by Riemannian metric and scalar curvature, respectively. Design/methodology/approach Topology and manifolds application-based methodology establishes the resultant drone. The trajectory planning and its optimization are controlled by the system of evolution equation over Ricci flow entropy. The navigation follows the Riemannian metric-based optimal path with an angular trajectory in the range from 0° to 360°. The obstacle avoidance in static and dynamic environments is controlled by the metric tensor and curvature tensor, respectively. The in-house drone is developed and coded using C++. For comparison of the real-time results and simulation results in static and dynamic environments, the simulation study has been conducted using MATLAB software. The proposed controller follows the topological programming constituted with manifold-based objective function and Riemannian metric, and scalar curvature-based constraints for linear and nonlinear navigation, respectively. Findings This proposed study demonstrates the possibility to develop the new topology-based efficient path planning approach for navigation of drone and provides a unique way to develop an innovative system having characteristics of static and dynamic obstacle avoidance and moving goal chasing in an uncertain environment. From the results obtained in the simulation and real-time environments, satisfactory agreements have been seen in terms of navigational parameters with the minimum error that justifies the significant working of the proposed controller. Additionally, the comparison of the proposed navigational controller with the other artificial intelligent controllers reveals performance improvement. Originality/value In this study, a new topological controller has been proposed for drone navigation. The topological drone navigation comprises the effective speed control and collision-free decisions corresponding to the Ricci flow equation and Ricci curvature over the Riemannian metric, respectively.

Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves better performance by balancing its essential intelligent behaviours more intelligently. AEPSO incorporates knowledge with the aim of choosing proper behaviors in each situation. This study provides a comparison between the variations of Basic PSO and AEPSO aiming to address dynamic and time dependent constraint problems in simulated robotic search. The problem is set up in a multi-robot learning scenario. The scenario is based on the use of a team of simulated robots (hereafter referred to as agents) who participate in survivor rescuing missions. The experiments are classified into three simulations. At first, agents employ variations of basic PSO as their decision maker and movement controllers. The first simulation investigates the impacts of swarm size, parameter adjustment, and population density on agents’ performance. Later, AEPSO is employed to improve the performance of the swarm in the same simulations. The final simulation investigates the feasibility of AEPSO in time-dependent, dynamic and uncertain environments. As shown by the results, AEPSO achieves an appreciable level of performance in dynamic, time-dependence and uncertain simulated environments and outperforms the variations of basic PSO, Linear Search and Random Search used in the simulations.

Organizations around the world continuously confront a wide range of challenges, risks and obstructions occurring in dynamic and complex business environment. These vulnerabilities keep on menacing their sustainability and appropriate functioning. Given such uncertain external pressures, the capabilities of employees working in teams within these organizations have become crucial and by time evolving as their viable assets. Business leaders are often tested for their agility in response, resource base sufficiency and innovative outcomes through unpredicted crises. Leading in tough times is itself a toughest challenge in face of unparalleled environmental pressures. It is therefore important to understand and explore what entails a successful leadership role for teams to remain innovative given any drastic change occurs. The objective of intended research was to address gaps in the understanding and comprehension of team innovation through leadership and leaders’ emotional intelligence in order to identify how leaders derive their teams in uncertain business environments. This study determined the influence of transformational leadership on team innovation with emotional intelligence of leaders as a moderating variable. The results showed that a relationship exists between transformational leadership and innovation in teams in an uncertain environment. Transformational leadership boosted employee innovation in teams. Accordingly, emotional intelligence has been seen as ability of leaders to make a set of norms that recognize employees in teams; yet, unpredicted effect of emotional intelligence on the relationship between transformational leadership and team innovation has been witnessed in this research.

A major goal of air traffic management is to strategically control the flow of traffic so that the demand at an airport meets but does not exceed the operational capacity in a dynamic environment. This paper uses the concept of receding horizon control (RHC) to conduct real-time planning for airport capacity management (ACM). It is shown that RHC provides a generic and flexible framework for developing real-time allocation algorithms for airport capacity in a dynamic and uncertain environment, and existing approaches such as the one step ahead adjustment can be considered as special cases of this approach. Robustness against the change of the environment and demands and computational efficiency are two advantages when applying RHC to the ACM problem, which are illustrated by a case study.

In this paper, we study the cooperative autonomous inspection by Unmanned Aerial Vehicles (UAV) swarms in dynamic and uncertain environments. Facing the challenges of spatio-temporally random target events and constrained onboard resources, we propose a distributed online path planning algorithm based on a Hybrid Artificial Potential Field (HAPF). In contrast to existing works relying on a single probability map, we construct a hybrid artificial potential field to integrate a probability map and an exploration map. The probability map, generated via Kernel Density Estimation from historical and real-time data, learns the distribution pattern of events, while the exploration map encourages coverage of unvisited areas. Virtual attractive forces guide UAVs to prioritize high-probability events and explore unknown regions, whereas virtual repulsive forces ensure collision-free cooperation. Moreover, a Hungarian algorithm based dynamic task allocation method is proposed to prevent resource redundancy. To tackle the well-known local minimum problem in potential field, a sticking release mechanism is designed to escape local optima. Simulation results demonstrate that, in comparison to existing algorithms, our method improving the task completion rate by 20 % and shortening the average completion time by 13 %, offering a practical solution for resource-constrained UAV swarms to achieve efficient and safe autonomous inspection in complex and dynamic environments.

Composite particle optimization with hyper-reflection scheme in dynamic environments

Aircraft routing problem is a crucial component for flight automation. Despite recent successes, challenges still remain when the environment is dynamic and uncertain. In this paper, we tackle the following two challenges. First, when the environment is uncertain, it is much safer if the route planner can guarantee a specified level of safety. Second, when the environment is dynamic, the planner needs to adapt to the changes in the environment quickly. To address these challenges, we present three contributions. First, we propose formulating the aircraft routing problem under a dynamic and uncertain environment as a chance constrained stochastic shortest path (CC-SSP) problem. Second, we introduce an anytime algorithm for the CC-SSP problem, which is effective in a dynamic environment with limited planning time. To be more specific, we present two versions of the algorithm and compare their performances. Third, we show that the algorithm can be generalized to solve a larger class of problems called chance constrained partially observable Markov decision process (CC-POMDP).

The major goal of air traffic management is to strategically control the flow of airport traffic. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation in airport environment. This paper uses the concept of dynamic receding horizon control (DRHC) to conduct real-time planning for airport capacity management. A feasible algorithm is then designed from DRHC point of view. It is shown that DRHC provides a generic and flexible framework for developing real-time allocation algorithms for airport capacity in a dynamic and uncertain environment. A simulation study shows that the algorithm is effective and efficient to solve the problem.

Unmanned Aerial Vehicles (UAVs) have become essential tools across critical domains, including infrastructure inspection, public safety monitoring, traffic surveillance, environmental sensing, and target tracking, owing to their ability to collect high-resolution spatial data rapidly. However, maintaining stable and accurate flight trajectories remains a significant challenge, particularly during autonomous missions in dynamic or uncertain environments. This study presents a novel flight path prediction framework based on Gated Recurrent Units (GRUs), designed for both single-step and multi-step-ahead forecasting of four-dimensional UAV coordinates, Easting (X), Northing (Y), Altitude (Z), and Time (T), using historical sensor flight data. Model performance was systematically validated against traditional Recurrent Neural Network architectures. On unseen test data, the GRU model demonstrated enhanced predictive accuracy in single-step prediction, achieving a MAE of 0.0036, Root Mean Square Error (RMSE) of 0.0054, and a (R2) of 0.9923. Crucially, in multi-step-ahead forecasting designed to simulate real-world challenges such as GPS outages, the GRU model maintained exceptional stability and low error, confirming its resilience to error accumulation. The findings establish that the GRU-based model is a highly accurate, computationally efficient, and reliable solution for UAV trajectory forecasting. This framework enhances autonomous navigation and directly supports the data integrity required for high-fidelity photogrammetric mapping, ensuring reliable site assessment in complex and dynamic environments.

This paper presents the evolutionary programming with an ensemble of memories to deal with optimization problems in dynamic environments. The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving directions. Diversity of the population is enhanced by an ensemble of external archives that serve as short-term and long-term memories. The archive members also act as the basic solutions when environmental changes occur. The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results are presented.

ENSOR Networks represent a new frontier in technology that promises to push traditional computation beyond the digital abstractions of cyberspace to interact with the real world in human timeframes. Miniature computational devices, often embedded in mobile wireless platforms, interact directly with the physical world, cognizant of a common mission, spanning time and space to monitor changes in the operational environment, and collaborating to actuate distributed tasks in dynamic and uncertain environments. While once the fascinating stuff of science fiction, sensor networks are rapidly becoming the reality that captivates the imagination of researchers and practitioners to enable inexpensive devices to act as numerous eyes and ears of soldiers in surveying a hostile battlefield from a safe distance or to track bio/chemical plumes in the environment for homeland security. Mobile robots with embedded sensor systems explore the surface of Mars and integrated systems of undersea robots are being designed to develop high fidelity nowcasts and forecasts of the ocean through time-space coordinated sampling or to hunt for mines or handle hazardous materials. In general, the next phase of automation calls on networks of sensors to take on the dull, dirty, and dangerous functions of human interest, and to accomplish them with the perception and adaptation of humans and in collaboration with humans. Sensors of physical phenomena with integrated servomechanisms have been commonplace throughout the latter half of the 20th century, controlling thermostats and valves, monitoring flow or adapting to changes in pressure or stress, and providing alarms for fire or flooding. They have been expected to perform these and many other localized isolated tasks with precision and reliability. The distinction of present day demands on sensor networks is in the comprehensive perception of locally sensed changes in the physics of the environment and adaptive time-space coordinated activity of individual servo-mechanisms in support of a common mission. This special section deals with recent advances in the study of Sensor Networks as interacting autonomous mobile sensor nodes. The objective is to address the engineering design issues for achieving dependable performance through dynamic distributed collaboration of many inexpensive, low reliability sensors with limited sensing and communication ranges. Advances in integrated wireless communications, fast servo-controlled sensors/actuators, and micro and nano technologies have together enabled inexpensive devices, often on mobile platforms, to be air dropped or deployed in unknown or dynamic environments. These devices are expected to self-organize and form ad hoc networks to continuously survey a battlefield for enemy targets over long periods of time, conserving precious resources unless some enemy activity is detected. Upon detection, the nodes form dynamic clusters to localize and track enemy targets. Traditional programming, computation, communication, and control techniques must all advance to comprehend the distributed dynamics of the environment and actuate a timely response. Research papers in this section address design trade offs for situation awareness, adaptive and dependable infrastructure, and coordinated inference in mission-oriented mobile sensor networks. The first paper by Bergamo, Asgari, Wang, Maniezzo, Yip, Hudson, Yao, and Estrin solves the far field acoustic source localization problem through beamforming. Waveforms originating at a given source are used by a set of spatially separated acoustic sensors to localize the source through time synchronized estimates of direction of arrival. Experiments in free space and reverberant scenarios demonstrate the power of very low cost devices to achieve sophisticated space-time operation in real-time.

Social Dilemmas: Individual, Collective, and Dynamic Perspectives.- Social Dilemmas: Individual, Collective, and Dynamic Perspectives..- 1 Collective Perspective Provision of Resources.- Collective Risk Generation and Risk Management: Unexploited Potential of the Social Dilemmas Paradigm..- Attitudes toward Public Affairs in a Society in Transition..- Provision of Community Social Services: Role of Distributive Fairness for Willingness to Pay..- Concentration and Dispersion of Resources in Simulated Organizations Characterized by Equal or Unequal Power Relations..- 2 Individual Perspective Cooperation in Experimental Games.- Why do 'Cooperators' Cooperate?: Efficacy as a Moderator of Social Motive Effects..- Tacit Coordination and Fairness Judgments in Social Dilemmas..- Does Knowing the Jointly Rational Solution Make You Want to Pursue it? Motivational Orientation, Information, and Behavior in two Social Dilemmas..- Reduction of Environmental Risk as a Public Good..- Hostage Posting as a Mechanism for Co-operation in the Prisoner'sDilemma Game..- Effect of Threshold Level on Greed, Fear, and Cooperation in Step-level, Give-some and Take-some Dilemmas..- Does The Motivating Power of Loss Exist? An Experimental Test of the Effect of Losses on Cooperation..- 3 Dynamic Perspective Computer Simulations of Micro-macro Dynamics.- Computer Simulation of Cooperative Decision Making..- Natural Selection and Social Learning in Prisoner'sDilemma: Co-adaptation with Genetic Algorithms and Artificial Neural Networks..- Foundations of Rational Interaction in Cognitive Agents: a Computational Approach..- Computer Simulation of Social Value Orientation: Vitality, Satisfaction, and Emergent Game Structures..- Generalized Exchange Perspective on the Evolution of Altruism..- Social Dilemmas in Lineland and Flatland..- Selective Play: Social Embeddedness of Social Dilemmas..- 'self-organizing' Friendship Networks..- Evolution of Cooperation in a Simulated Inter-group Conflict.

A sequential irrigation scheduling problem is the problem of preparing a schedule to sequentially service a set of water users. This problem has an analogy with the classical single machine earliness/tardiness scheduling problem in operations research. In previously published work, integer program and heuristics were used to solve sequential irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding (NP-hard). This is widely reported in operations research. Hence, integer program can only be used to solve relatively small problems usually in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms (GA), simulated annealing, or tabu search methods need to be used. These need to be formulated carefully and tested thoroughly. The current research is to explore the potential of GA to solve the sequential irrigation scheduling problems. Four GA models are presented that model four different sequential irrigation scenarios. The GA models are tested extensively for a range of problem sizes, and the solution quality is compared against solutions from integer programs and heuristics. The GA is applied to the practical engineering problem of scheduling water scheduling to 94 water users.

Beyond the prisoner's dilemma: Toward models to discriminate among mechanisms of cooperation in nature

Theoretical investigations and quasi-experimental evidence from modern conflict areas suggest that intergroup conflict and ostracism play a pivotal role in the evolution of cooperation. However, there is little direct evidence about the influence of intergroup conflict on human social behavior in the presence of endogenous group formation and unrestricted migration. This study introduces an experiment to examine the impact of intergroup conflict and ostracism on group formation, and human cooperation in a dynamic environment where group size, the occurrence of intergroup hostility and the threat of ostracism are endogenously determined. Here, we show that intergroup conflict may inhibit merging into single large human coalitions. The threat of ostracism is shown to increase the average group size and cooperation within the society. In addition, we find that competitive pressure between groups decreases the likelihood of social exclusion. Our results suggest that free migration between groups suppresses the impact of between-group competition on within-group cooperation. Moreover, our results stress the role of ostracism as a means to repress competition within groups and enhance group success in competition against other groups.