The Experts below are selected from a list of 41241 Experts worldwide ranked by ideXlab platform
Khaled Sedraoui - One of the best experts on this subject based on the ideXlab platform.
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Scheduling Robotic Cellular Manufacturing Systems With Timed Petri Net, A* Search, and Admissible Heuristic Function
IEEE Transactions on Automation Science and Engineering, 2020Co-Authors: Bo Huang, Mengchu Zhou, Abdullah Abusorrah, Khaled SedraouiAbstract:System scheduling is a decision-making process that plays an important role in improving the performance of robotic cellular manufacturing (RCM) systems. Timed Petri nets (PNs) are a formalism suitable for graphically and concisely modeling such systems and obtaining their reachable state graphs. Within their reachability graphs, timed PNs' evolution and intelligent search algorithms can be combined to find an efficient operation sequence from an initial state to a goal one for the underlying systems of the nets. To schedule RCM systems, this work proposes an A* search with a new Heuristic Function based on timed PNs. When compared with related approaches, the proposed one can deal with token remaining time, weighted arcs, and multiple resource copies commonly seen in the PN models of RCM systems. The admissibility of the proposed Heuristic Function is proved. Finally, experimental results are given to show the effectiveness and efficiency of the proposed method and Heuristic Function.
Bo Huang - One of the best experts on this subject based on the ideXlab platform.
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Scheduling Robotic Cellular Manufacturing Systems With Timed Petri Net, A* Search, and Admissible Heuristic Function
IEEE Transactions on Automation Science and Engineering, 2020Co-Authors: Bo Huang, Mengchu Zhou, Abdullah Abusorrah, Khaled SedraouiAbstract:System scheduling is a decision-making process that plays an important role in improving the performance of robotic cellular manufacturing (RCM) systems. Timed Petri nets (PNs) are a formalism suitable for graphically and concisely modeling such systems and obtaining their reachable state graphs. Within their reachability graphs, timed PNs' evolution and intelligent search algorithms can be combined to find an efficient operation sequence from an initial state to a goal one for the underlying systems of the nets. To schedule RCM systems, this work proposes an A* search with a new Heuristic Function based on timed PNs. When compared with related approaches, the proposed one can deal with token remaining time, weighted arcs, and multiple resource copies commonly seen in the PN models of RCM systems. The admissibility of the proposed Heuristic Function is proved. Finally, experimental results are given to show the effectiveness and efficiency of the proposed method and Heuristic Function.
Joerg Hoffmann - One of the best experts on this subject based on the ideXlab platform.
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red black planning a new tractability analysis and Heuristic Function
Annual Symposium on Combinatorial Search, 2015Co-Authors: Daniel Gnad, Joerg HoffmannAbstract:Red-black planning is a recent approach to partial delete relaxation, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. Practical Heuristic Functions can be generated from tractable sub-classes of red-black planning. Prior work has identified such sub-classes based on the black causal graph, i.e., the projection of the causal graph onto the black variables. Here, we consider cross-dependencies between black and red variables instead. We show that, if no red variable relies on black preconditions, then red-black plan generation is tractable in the size of the black state space, i.e., the product of the black variables. We employ this insight to devise a new red-black plan Heuristic in which variables are painted black starting from the causal graph leaves. We evaluate this Heuristic on the planning competition benchmarks. Compared to a standard delete relaxation Heuristic, while the increased runtime overhead often is detrimental, in some cases the search space reduction is strong enough to result in improved performance overall.
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SOCS - Red-Black Planning: A New Tractability Analysis and Heuristic Function
2015Co-Authors: Daniel Gnad, Joerg HoffmannAbstract:Red-black planning is a recent approach to partial delete relaxation, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. Practical Heuristic Functions can be generated from tractable sub-classes of red-black planning. Prior work has identified such sub-classes based on the black causal graph, i.e., the projection of the causal graph onto the black variables. Here, we consider cross-dependencies between black and red variables instead. We show that, if no red variable relies on black preconditions, then red-black plan generation is tractable in the size of the black state space, i.e., the product of the black variables. We employ this insight to devise a new red-black plan Heuristic in which variables are painted black starting from the causal graph leaves. We evaluate this Heuristic on the planning competition benchmarks. Compared to a standard delete relaxation Heuristic, while the increased runtime overhead often is detrimental, in some cases the search space reduction is strong enough to result in improved performance overall.
Rabi N Mahapatra - One of the best experts on this subject based on the ideXlab platform.
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Heuristic Function evolution for pathfinding algorithm in fpga accelerator
International Conference on Artificial Intelligence, 2020Co-Authors: Ying Fung Yiu, Rabi N MahapatraAbstract:A* is an informed pathfinding algorithm that depends on an accurate Heuristic Function to search for the shortest path. A complex pathfinding problem requires a well-informed Heuristic Function to efficiently process all data and compute the next move. Hence, designing good Heuristic Functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. However, designing new Heuristic Functions is time consuming and difficult. Evolutionary Heuristic A* (EHA*) search proposed to have a self-evolving Heuristic Function to reduce the engineering efforts on Heuristic Functions design. The Genetic Algorithm is one of the most popular and efficient optimization techniques that is based on the Darwinian principle of survival of the fittest. It has been successfully applied on many complex real world applications including VLSI circuit partitioning, Travelling Salesman Problem (TSP), and robotic designs. Although the Genetic Algorithm is proved to be efficient on solving complex problems, the amount of computations and iterations required for this method is enormous. Therefore, we propose a hardware accelerator architecture for EHA* that is implemented on a Field Programmable Gate Array(FPGA) by employing a combination of pipelining and parallelization to achieve better performance. Moreover, the proposed Genetic Algorithm accelerator can be customized in terms of the population size, number of generations, crossover rates, and mutation rates for flexibility. The FPGA accelerator proposed in this paper achieves more than 8x speed up compared to the software implementation.
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AIKE - Heuristic Function Evolution For Pathfinding Algorithm in FPGA Accelerator
2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2020Co-Authors: Ying Fung Yiu, Rabi N MahapatraAbstract:A* is an informed pathfinding algorithm that depends on an accurate Heuristic Function to search for the shortest path. A complex pathfinding problem requires a well-informed Heuristic Function to efficiently process all data and compute the next move. Hence, designing good Heuristic Functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. However, designing new Heuristic Functions is time consuming and difficult. Evolutionary Heuristic A* (EHA*) search proposed to have a self-evolving Heuristic Function to reduce the engineering efforts on Heuristic Functions design. The Genetic Algorithm is one of the most popular and efficient optimization techniques that is based on the Darwinian principle of survival of the fittest. It has been successfully applied on many complex real world applications including VLSI circuit partitioning, Travelling Salesman Problem (TSP), and robotic designs. Although the Genetic Algorithm is proved to be efficient on solving complex problems, the amount of computations and iterations required for this method is enormous. Therefore, we propose a hardware accelerator architecture for EHA* that is implemented on a Field Programmable Gate Array(FPGA) by employing a combination of pipelining and parallelization to achieve better performance. Moreover, the proposed Genetic Algorithm accelerator can be customized in terms of the population size, number of generations, crossover rates, and mutation rates for flexibility. The FPGA accelerator proposed in this paper achieves more than 8x speed up compared to the software implementation.
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Hierarchical Evolutionary Heuristic A* Search
2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI), 2020Co-Authors: Ying Fung Yiu, Rabi N MahapatraAbstract:A* is an informed pathfinding algorithm that uses a Heuristic Function to determine the best action to take based on the given information. The performance of A* is heavily dependent on the quality of the Heuristic Function. The Heuristic Function determines the search speed, accuracy, and memory consumption. Hence, designing good Heuristic Functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. However, designing new Heuristic Functions is a difficult task, especially when they are used to solve complex problems. Moreover, a single Heuristic Function might not be enough to digest all the provided information and return the best guidance during the search. Previous works suggest that multiple Heuristics for complex problems can dramatically speed up the search. However, choosing the appropriate combination of Heuristic Functions is tricky. Current optimization approaches rely on hand-tuning the parameters via trial and error by the engineers over many iterations. There is a need to reduce the difficulty of designing Heuristic Functions for search performance maximization. In this paper, we develop a novel Heuristic search called Hierarchical Evolutionary Heuristic A* (HEHA*) where multiple Heuristics are chosen and evolved using Genetic Algorithm. HEHA* combines the techniques of map abstraction, pattern database, and Heuristic improvement. The advantage of HEHA* is twofold: 1) it partitions and reduces the search space based on local features to speed-up the search, and 2) it automatically designs and optimizes Heuristics for different local regions to maximize the search performance. We test our algorithm on a widely used grid-based map benchmark to compare with A* variants. Our results show that HEHA* outperforms compared with other pathfinding algorithms in terms of execution time and memory consumption.
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Evolutionary Heuristic A* Search: Pathfinding Algorithm with Self-Designed and Optimized Heuristic Function
International Journal of Semantic Computing, 2019Co-Authors: Ying Fung Yiu, Rabi N MahapatraAbstract:The performance and efficiency of A* search algorithm heavily depends on the quality of the Heuristic Function. Therefore, designing an optimal Heuristic Function becomes the primary goal of develo...
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evolutionary Heuristic a search Heuristic Function optimization via genetic algorithm
International Conference on Artificial Intelligence, 2018Co-Authors: Ying Fung Yiu, Rabi N MahapatraAbstract:The performance and efficiency of A* search algorithm heavily depend on the quality of the Heuristic Function. Therefore, designing an optimal Heuristic Function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed Heuristic Function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a Heuristic Function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches often avoids complex Heuristic Function design: they either trade-off the accuracy for faster computation or taking advantage of the parallelism for better performance. The objective of this paper is to reduce the difficulty of complex Heuristic Function design for A* search algorithm. We aim to design an algorithm that can be automatically optimized to achieve rapid search with high accuracy and low computational cost. In this paper, we present a novel design and optimization method for a Multi-Weighted-Heuristics Function (MWH) named Evolutionary Heuristic A* search (EHA*) to: 1) minimize the effort on Heuristic Function design via Genetic Algorithm (GA), 2) optimize the performance of A* search and its variants including but not limited to WA* and MHA*, and 3) guarantee the completeness and optimality. EHA* algorithm enables high performance searches and significantly simplifies the processing of Heuristic design. We apply EHA* to two classic AI search problems: the Blocks World and the Sliding Tile Puzzle. Our experiment result shows that EHA* 1) is capable to choose an accurate Heuristic Function that provides an optimal solution, 2) can identify and eliminate inefficient Heuristics, 3) is able to automatically design multi-Heuristics Function, and 4) minimize both the time and space complexity.
Ku Ruhana Ku-mahamud - One of the best experts on this subject based on the ideXlab platform.
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New Heuristic Function in Ant Colony System for the Travelling Salesman Problem
2013Co-Authors: Mustafa Muwafak Alobaedy, Ku Ruhana Ku-mahamudAbstract:Ant Colony System (ACS) is one of the best algorithms to solve NP-hard problems. However, ACS suffers from pheromone stagnation problem when all ants converge quickly on one sub-optimal solution. ACS algorithm utilizes the value between nodes as Heuristic values to calculate the probability of choosing the next node. However, one part of the algorithm, called Heuristic Function, is not updated at any time throughout the process to reflect the new information discovered by the ants. This paper proposes an Enhanced Ant Colony System algorithm for solving the Travelling Salesman Problem. The enhanced algorithm is able to generate shorter tours within reasonable times by using accumulated values from pheromones and Heuristics. The proposed enhanced ACS algorithm integrates a new Heuristic Function that can reflect the new information discovered by the ants. Experiments conducted have used eight data sets from TSPLIB with different numbers of cities. The proposed algorithm shows promising results when compared to classical ACS in term of best, average, and standard deviation of the best tour length.
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Ant Colony System with Heuristic Function for the Travelling Salesman Problem
Journal of Next Generation Information Technology, 2013Co-Authors: Mustafa Muwafak Alobaedy, Ku Ruhana Ku-mahamudAbstract:Abstract Ant colony system which is classified as a meta-Heuristic algorithm is considered as one of the best optimization algorithm for solving different type of NP-Hard problem including the travelling salesman problem. A Heuristic Function in the Ant colony system uses pheromone and distance values to produce Heuristic values in solving the travelling salesman problem. However, the Heuristic values are not updated in the entire process to reflect the knowledge discovered by ants while moving from city to city. This paper presents the work on enhancing the Heuristic Function in ant colony system in order to reflect the new information discovered by the ants. Experimental results showed that enhanced algorithm provides better results than classical ant colony system in term of best, average and standard of the best tour length. Keywords : Ant Colony Optimization, Ant Colony System, Heuristic Function, Traveling Salesman Problem 1. Introduction Biological ants have the ability to discover the shortest route from the nest to the source of food [1]. Although they do not have an advanced vision system [2], they have the ability to communicate with the environment. Ants use a chemical substance called a “pheromone” to communicate with the environment and between each other [3]. Pheromone substance has an evaporation property which is a powerful mechanism to update the route information. While an ant moves looking for food, it deposits a pheromone along the path. The following ant will, more likely, select the route with richer pheromones. This mechanism will make the ant choose the shortest path. In 1992, Marco Dorigo proposed the first Ant Colony Optimization (ACO) algorithm to search for an optimal solution in graphs to solve optimization problems such as the travelling salesman problem, job scheduling and network routing [1]. The variants of ACO are: (i) Ant System (AS) [4] [5] [6]. (ii) The first improvement on the ant system, called the Elitist strategy for Ant System (EAS) [7]. The improvement was done by providing strong additional reinforcement to the arcs belonging to the best tour found since the start of the algorithm. (iii) Rank-Based Ant System (AS
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New Heuristic Function in ant colony system for job scheduling in grid computing
2012Co-Authors: Ku Ruhana Ku-mahamud, Mustafa Muwafak AlobaedyAbstract:Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as Heuristic and meta-Heuristic algorithms.Ant colony system algorithm is a meta-Heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its Heuristic Function which affects the algorithm behavior in terms of finding the shortest connection between edges.This paper focuses on enhancing the Heuristic Function where information about recent ants’ discoveries will be taken into account.Experiments were conducted using a simulator with dynamic environment features to mimic the grid environment.Results show that the proposed enhanced algorithm produce better output in term of utilization and make span.
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New Heuristic Function in ant colony system algorithm
2012Co-Authors: Ku Ruhana Ku-mahamud, Yuhanif Yusof, Massudi Mahmuddin, Mustafa Muwafak AlobaedyAbstract:NP-hard problem can be solved by Ant Colony System (ACS) algorithm.However, ACS suffers from pheromone stagnation problem, a situation when all ants converge quickly to one sub-optimal solution.ACS algorithm utilizes the value between nodes as Heuristic value to calculate the probability of choosing the next node.However, the Heuristic value is not updated throughout the process to reflect new information discovered by the ants.This paper proposes a new Heuristic Function for the Ant Colony System algorithm that can reflect new information discovered by ants.The credibility of the new Function was tested on travelling salesman and grid computing problems.Promising results were obtained when compared to classical ACS algorithm in terms of best tour length for the travelling sales-man problem. Better results were also obtained for the grid scheduling problem in terms of makespan and utilization.
