Heuristic Research Articles

When to Switch: Planning and Learning for Partially Observable Multi-Agent Pathfinding.

Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where the environment is only partially observable for each agent, i.e., an agent observes the obstacles and other agents only within a limited field-of-view. Moreover, we assume that the agents do not communicate and do not share knowledge on their goals, intended actions, etc. The task is to construct a policy that maps the agent's observations to actions. Our contribution is multifold. First, we propose two novel policies for solving partially observable MAPF (PO-MAPF): one based on heuristic search and another one based on reinforcement learning (RL). Next, we introduce a mixed policy that is based on switching between the two. We suggest three different switch scenarios: the heuristic, the deterministic, and the learnable one. A thorough empirical evaluation of all the proposed policies in a variety of setups shows that the mixing policy demonstrates the best performance is able to generalize well to the unseen maps and problem instances, and, additionally, outperforms the state-of-the-art counterparts (Primal2 and PICO). The source-code is available at https://github.com/AIRI-Institute/when-to-switch.

Search algorithms in state space for solving artificial intelligence problems

A comprehensive analysis of state space search algorithms widely used in artificial intelligence (AI) problems has been carried out. Various classes of algorithms: blind exhaustive and limited search methods, heuristic algorithms, metaheuristic approaches, as well as their hybrid combinations are considered. A comparison was made based on key criteria: search strategy, systematicity, optimality, computational complexity, scalability, parallelizability, etc. Areas of effective application of each type of algorithm are identified. Recommendations for choosing an appropriate strategy depending on the characteristics of the problem are formulated. Promising directions for the development of methods based on hybridization, as well as machine learning technologies for adaptive optimization of search parameters, are considered. Keywords artificial intelligence, state space, search algorithms, blind search, heuristic search, metaheuristics, hybrid algorithm, machine learning

Optimizing Artificial Neural Network Learning Using Improved Reinforcement Learning in Artificial Bee Colony Algorithm

Artificial neural networks (ANNs) are widely used machine learning techniques with applications in various fields. Heuristic search optimization methods are typically used to minimize the loss function in ANNs. However, these methods can lead the network to become stuck in local optima, limiting performance. To overcome this challenge, this study introduces an improved optimization approach, the improvement of reinforcement learning in the artificial bee colony (improved R‐ABC) algorithm, to enhance the optimization process for ANNs. The proposed method aims to overcome the limitations of heuristic search and improve the efficiency of weight adjustment in ANNs. This new approach enhances the discovery phase of the traditional R‐ABC by including the parameters of neighboring food sources, augmenting the search capabilities for finding the optimal solution. The performance of the improved R‐ABC was compared with ANNs utilizing backpropagation with stochastic gradient descent (SGD) and Adam optimizers, as well as other swarm intelligence (SI) methods such as particle swarm optimization (PSO) and traditional R‐ABC. The results showed that both PSO and R‐ABC continuously improved the solutions across all benchmark datasets. In the iris dataset, all SI approaches consistently achieved F1‐scores exceeding 0.94, outperforming SGD and Adam. For the other datasets, the SI approach generally outperformed the other optimization methods. The results indicate that when the improved R‐ABC is applied to ANNs, it outperforms heuristic search optimization, especially as the network size expands. Although SGD and Adam achieved faster execution times with TensorFlow, the study suggests that using PSO and improved R‐ABC can improve model accuracy and efficiency. Advanced SI methods enhance the optimization process and increase the ability of ANNs to obtain optimal solutions. Enhanced R‐ABC and PSO algorithms can significantly improve ANN training performance and efficiency, especially in complex and high‐dimensional datasets.

Testing the Waters: Founding Team Composition and Search Heuristics in Academic Entrepreneurial Ventures

Testing the Waters: Founding Team Composition and Search Heuristics in Academic Entrepreneurial Ventures

Chemoenzymatic multistep retrosynthesis with transformer loops.

Integrating enzymatic reactions into computer-aided synthesis planning (CASP) should help devise more selective, economical, and greener synthetic routes. Herein we report the triple-transformer loop algorithm with biocatalysis (TTLAB) as a new CASP tool for chemo-enzymatic multistep retrosynthesis. Single-step retrosyntheses are performed using two triple transformer loops (TTL), one trained with chemical reactions from the US Patent Office (USPTO-TTL), the second one obtained by multitask transfer learning combining the USPTO dataset with preparative biotransformations from the literature (ENZR-TTL). Each TTL performs single-step retrosynthesis independently by tagging potential reactive sites in the product, predicting for each site possible starting materials (T1) and reagents or enzymes (T2), and validating the predictions via a forward transformer (T3). TTLAB combines predictions from both TTLs to explore multistep sequences using a heuristic best-first tree search and propose short routes from commercial building blocks including enantioselective biocatalytic steps. TTLAB can be used to assist chemoenzymatic route design.

A proposal towards a VNS-based decision support tool for large scale location-covering-type problems

We describe a number of typical features, constraints and objectives, frequently appearing in spatial system-design of maximum covering-type, such as emergency response systems. We then indicate some heuristic local search methods topped by a Variable Neighbourhood Search to construct and search for good solutions. These ideas were intended to form the basis for a possibly multi-objective spatial decision support system.

Fast and Adaptive Multi-Agent Planning under Collaborative Temporal Logic Tasks via Poset Products

Efficient coordination and planning is essential for large-scale multi-agent systems that collaborate in a shared dynamic environment. Heuristic search methods or learning-based approaches often lack the guarantee on correctness and performance. Moreover, when the collaborative tasks contain both spatial and temporal requirements, e.g., as linear temporal logic (LTL) formulas, formal methods provide a verifiable framework for task planning. However, since the planning complexity grows exponentially with the number of agents and the length of the task formula, existing studies are mostly limited to small artificial cases. To address this issue, a new planning paradigm is proposed in this work for system-wide temporal task formulas that are released online and continually. It avoids two common bottlenecks in the traditional methods, i.e., (a) the direct translation of the complete task formula to the associated Büchi automaton and (b) the synchronized product between the Büchi automaton and the transition models of all agents. Instead, an adaptive planning algorithm is proposed, which computes the product of relaxed partially ordered sets (R-posets) on-the-fly and assigns these subtasks to the agents subject to the ordering constraints. It is shown that the first valid plan can be derived with a polynomial time and memory complexity with respect to the system size and the formula length. Our method can take into account task formulas with a length of more than 400 and a fleet with more than 400 agents, while most existing methods fail at the formula length of 25 within a reasonable duration. The proposed method is validated on large fleets of service robots in both simulation and hardware experiments.

An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory.

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of $ k $-step $ R $-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency.

Practical Uplink Multi-User MIMO Scheduling Based on Iterative Heuristic Search

Practical Uplink Multi-User MIMO Scheduling Based on Iterative Heuristic Search

Analysis of Optimal Power Flow Using Combined PSO-GSA Technique

Using a hybrid approach that incorporates both particle swarm optimization (PSO) and gravitational search algorithms (GSA), this project aims to find the best way for power systems to distribute their energy. A novel heuristic search optimization technique, the GSA is works on the law of gravity. While this strategy has many advantages, it suffers from sluggish search performance and memory constraints. In order to discover a solution to this problem, the PSO technique was utilized. PSO and GSA, were utilized in this investigation to discover the optimal power flow utilizing a combination of these two methodologies. The suggested optimization method merges the social thinking and local search features of particle swarm optimization with those of the GSA, therefore taking benefit of both algorithms. This study examines and evaluates an optimization technique for the optimal power flow problem, focusing on reducing fuel costs, improving the voltage profile, and minimizing real power losses. The investigation and assessment are conducted on the commonly used IEEE 30-bus test systems. The simulation findings showcase the robust and effective resolution of the optimum power transfer problem through the amalgamation of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA).

Scheduling parallel extrusion lines

This paper introduces the problem of scheduling jobs on parallel plastic extrusion lines where each line is composed of one or more than one extruder. Although there are some similarities between the introduced problem and the non-identical parallel machines scheduling problems with sequence-dependent setup times, limited additional resources and machine eligibility restrictions, the problem considered in this paper is a generalization of the parallel machine scheduling problem. This is because in parallel machines scheduling each job requires only one machine but in our case some jobs require more than one machine. Thus, our problem reduces to the parallel machine scheduling problem if all jobs require only one machine. This paper describes the problem of scheduling parallel extrusion lines, its industrial context, and develops a mixed-linear formulation to model the problem. This formulation allowed solving instances of up to 15 jobs. In addition, we developed four metaheuristics: a simulated annealing algorithm, a tabu search heuristic, a genetic algorithm, and a greedy randomized adaptive search procedure. These metaheuristics can be used to solve real-life instances of the problem. A numerical experiment shows that the proposed metaheuristics produce excellent solutions. Some of the proposed simulated annealing adaptations and of the tabu search heuristics obtained solutions with less than 2% deviation from the optimum.

ИСТОРИЯ И КУЛЬТУРА ДЖЕГОНАССКИХ ГОРСКИХ ЕВРЕЕВ В СОВРЕМЕННЫХ ЦИФРОВЫХ АРХИВАХ / БАЗАХ ДАННЫХ (ИСТОЧНИКОВЕДЧЕСКИЙ АСПЕКТ)

During two decades, the Databases of digitized historical documents had come into sight, specifi-cally WWI and WWII periods (www.gwar.mil.ru; www.obd-memorial.ru; www.podvignaroda.ru; www.pamyat-naroda.ru). It’s bringing a new momentum in the historical and ethnological studies by updating the focus on individual biographies, family stories and their network, genealogies and onomastics. The author is showcasing the potential of Databases for heuristic search on the local Mountain Jews group in Dzhegonas village, Kuban region. It is noted that there is an interaction process between the researcher and the platform, in which the platform, after providing options for the required data, can change the vector and focus of scientific search, thereby changing the object and subject of research, among other things. It is concluded that the advent of such Internet platforms of digitized documents and Databases affects the complex of sources in history and eth-nology on the one hand, and influences research processes on the other.

A Hybrid GAPSO Optimization Approach

In this work, the hybrid techniques of genetic algorithm (GA) and particle swarm optimization (PSO) are presented. PSO and GA are two population-based heuristic search methods that can be applied to the channel allocation optimization problem. GAPSO is based on a mixture of particle swarm optimization (PSO) and genetic algorithms (GA). Individuals of a new generation are produced in GAPSO by PSO in addition to crossover and mutation operations as in GA. In order to reduce the number of blocked calls and handoff failures in the mobile network, the Hybrid GAPSO algorithm is used to allocate tasks to resources efficiently. The proposed strategy optimizes the channel allocation using the GAPSO.

An enhanced memetic algorithm with hierarchical heuristic neighborhood search for type-2 green fuzzy flexible job shop scheduling

The green flexible job shop has received increasing attention due to the development of modern industry and the improvement of environmental protection awareness. Meanwhile, in customized manufacturing, the processing time cannot be exactly predicted. Therefore, it can be predicted by the type-2 fuzzy system, which can effectively characterize the uncertainty of processing time. This work aims to solve the green type-2 fuzzy flexible job shop problem (T2FJSP), considering minimizing total energy consumption (TEC) and the makespan simultaneously. Memetic algorithms are used for the T2FJSP, however, most of them do not allocate local search opportunities based on the quality of solutions, resulting in the waste of computing resources. In this paper, an enhanced memetic algorithm with hierarchical heuristic neighborhood search (EMAH) is proposed to overcome the shortcoming. EMAH has the following features: (i) a cooperative heuristic initialization is designed by combining six heuristics to generate high-quality solutions; (ii) a hierarchical heuristic neighborhood search strategy is proposed to better enhance the exploitation by allocating computing resources based on the quality of the solutions; and (iii) an energy-saving strategy is designed to decrease the makespan and TEC. The effectiveness of EMAH is verified by comparing it with the state-of-the-art algorithms in a T2FJSP benchmark. The results indicate the superiority of EMAH in solving the T2FJSP.

Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques

ABSTRACT The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.

Efficient and cost-effective method for identifying urban ventilation corridors using a heuristic search algorithm

Global temperatures are rapidly increasing, and one of the consequences of such increases is the urban heat island effect, which is a major problem. Enhancing urban ventilation is a key strategy for reducing urban temperatures. This study proposes a method for efficiently identifying urban ventilation corridors. Taichung City in central Taiwan was selected as the study area, and remote sensing technologies were used to observe and extract the urban building information in two sites. The collected information was used to develop maps of the roughness length (z0) in the city, and a heuristic search algorithm (A* search algorithm) was used to identify the urban ventilation corridors in the two sites and Taichung City overall. The results revealed that the remote sensing technologies were both accurate and cost-effective in obtaining building information, and the identified ventilation corridors were also accurate and exhibited higher-than-average wind speeds. Hence, the proposed method can be used to inexpensively and conveniently identify both corridors with high ventilation potential and poor ventilation that require urgent improvement. The method was further applied to identify ventilation corridors in the entirety of Taichung City, demonstrating the suitability of the proposed method for larger-scale applications.

BQA: a high-performance quantum circuits scheduling strategy based on heuristic search

BQA: a high-performance quantum circuits scheduling strategy based on heuristic search

A decision making framework for joint replenishment and delivery scheduling problems under mixed uncertainty

Concerning the essence of risk, a joint replenishment and delivery scheduling problem with fuzzy cost-related parameters and random number of imperfect quality items is developed to make it suitable for the inherent uncertainties of procurement-shipment process. The mathematical modelling-based decision system is formulated as a chance-constrained programming with the idea of embedding decision makers’ risk tolerance. Following this notion, the model is translated into an equivalent non-linear counterpart and a neighbourhood heuristic search is designed based on the properties of the cost function. We introduce an integrated cross-entropy algorithm, incorporating the heuristic in the cross-entropy framework, to solve it. The numerical results demonstrate that ICE is quite effective in comparison to state-of-the-art algorithms. Our framework is helpful for decision makers to determine economically acceptable performance objectives in the presence of uncertain issues, and thus to build resilience in supply chain.

Heuristic Search for Rank Aggregation with Application to Label Ranking

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and an enhanced late acceptance local search method reinforced by a relaxed acceptance and replacement strategy and a fast incremental evaluation mechanism. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on both synthetic and real-world benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, a well-established machine learning task. We additionally analyze several key algorithmic components to gain insight into their operation. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72371157] and Shanghai Pujiang Programme [Grant 23PJC069]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0019 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0019 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .