Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion

Bayili, Serhat; Polat, Faruk

doi:10.1016/j.knosys.2010.12.009

Cited by 63 publications

(21 citation statements)

References 20 publications

(36 reference statements)

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“…Bayili and Polat [15] introduced a multiobjective path planning (MOPP) algorithm, Limited damage A*, considering damage as a feasibility criterion in addition to distance. When an agent navigates in a threat zone, it is exposed to an additive damage.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

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MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives

Oral

Polat

2016

IEEE Trans. Cybern.

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The need for determining a path from an initial location to a target one is a crucial task in many applications, such as virtual simulations, robotics, and computer games. Almost all of the existing algorithms are designed to find optimal or suboptimal solutions considering only a single objective, namely path length. However, in many real life application path length is not the sole criteria for optimization, there are more than one criteria to be optimized that cannot be transformed to each other. In this paper, we introduce a novel multiobjective incremental algorithm, multiobjective D* lite (MOD* lite) built upon a well-known path planning algorithm, D* lite. A number of experiments are designed to compare the solution quality and execution time requirements of MOD* lite with the multiobjective A* algorithm, an alternative genetic algorithm we developed multiobjective genetic path planning and the strength Pareto evolutionary algorithm.

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Section: Related Work and Backgroundmentioning

confidence: 99%

“…After planning, consider that agent has found three paths with costs (15,260), (18,230), and (23,200). If there are more than one nondominated paths, one of them is selected randomly.…”

Section: B Partially Observable Testsmentioning

confidence: 99%

MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives

Oral

Polat

2016

IEEE Trans. Cybern.

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“…Using two hours per position, we tested IDA ⁄ and three different versions of SP-MCTS, subsequently called SP-MCTS (1), SP-MCTS (2), and SP-MCTS (3), on the standardized test set. IDA ⁄ applies the evaluation function described in Section 3.…”

Section: Comparison On the Standardized Test Setmentioning

confidence: 99%

“…(1) The selection strategy can be enhanced with RAVE [21] or progressive widening [13,17]. (2) This article demonstrated that combining small searches can achieve better scores than one large search. However, there is no information shared between the searches.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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Single-player Monte-Carlo tree search for SameGame

Schadd

Winands

Tak

et al. 2012

Knowledge-Based Systems

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a b s t r a c tClassic methods such as A ⁄ and IDA ⁄ are a popular and successful choice for one-player games. However, without an accurate admissible evaluation function, they fail. In this article we investigate whether Monte-Carlo tree search (MCTS) is an interesting alternative for one-player games where A ⁄ and IDA ⁄ methods do not perform well. Therefore, we propose a new MCTS variant, called single-player MonteCarlo tree search (SP-MCTS). The selection and backpropagation strategy in SP-MCTS are different from standard MCTS. Moreover, SP-MCTS makes use of randomized restarts. We tested IDA ⁄ and SP-MCTS on the puzzle SameGame and used the cross-entropy method to tune the SP-MCTS parameters. It turned out that our SP-MCTS program is able to score a substantial number of points on the standardized test set.

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Robust Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator for effective path planning in unmanned aerial vehicles

Kumar,

Saha

2023

Int J Communication

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SummaryUnmanned aerial vehicle (UAV) path planning can be treated as a nondeterministic polynomial (NP) hard concern or an optimization problem. The conventional approaches are unable to effectively handle these issues due to discontinuity, non‐linearity, multi‐modality, and inseparability. On the other hand, meta‐heuristic algorithms are effective at tackling these issues because they are simple, adaptable, and derivation free. To enhance the performance in a variety of challenging circumstances, this paper proposes a novel Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator (Q‐MOSFO‐CA) to solve the constrained UAV path planning issues. The multi‐objective functions considered here are costs and constraints (threat, terrain, turning, climbing, and gliding constraints) to determine the feasible and optimal path. To avoid the probability of falling into the local optimum and to address the shortcoming of unbalanced convergence and also to maintain the exploitation and exploration capability, the Cauchy operator (CA) is integrated with the sheep flock optimization (SFO) algorithm. The Q‐learning model is introduced to balance both the global and local searches. Here, the exploration model performs the global search whereas the exploitation model performs the local search to attain an optimal solution. In the simulation scenario, the statistical analysis is conducted under two scenarios, and some essential measures such as the number of iterations at convergence (NIC), evaluation time (ET), energy consumption, and convergence analysis are determined. The proposed method obtains NIC of 1305 and 1436, ET of 12.8 and 15.2 s, and energy consumption of 20,600 and 21,465 J for both Scenarios 1 and 2, respectively.

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Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion

Cited by 63 publications

References 20 publications

MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives

MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives

Single-player Monte-Carlo tree search for SameGame

Robust Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator for effective path planning in unmanned aerial vehicles

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