scholarly journals Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning Benchmarks

2005 ◽  
Vol 24 ◽  
pp. 685-758 ◽  
Author(s):  
J. Hoffmann
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Search Performance ◽  
Heuristic Function ◽  
Key Characteristics ◽  
Cost Surface ◽  
International Planning ◽  
Domain Independent ◽  
Successful Analysis

Between 1998 and 2004, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The unprecedented success of such methods, in many commonly used benchmark examples, calls for an understanding of what classes of domains these methods are well suited for. In the investigation at hand, we derive a formal background to such an understanding. We perform a case study covering a range of 30 commonly used STRIPS and ADL benchmark domains, including all examples used in the first four international planning competitions. We *prove* connections between domain structure and local search topology -- heuristic cost surface properties -- under an idealized version of the heuristic functions used in modern planners. The idealized heuristic function is called h^+, and differs from the practically used functions in that it returns the length of an *optimal* relaxed plan, which is NP-hard to compute. We identify several key characteristics of the topology under h^+, concerning the existence/non-existence of unrecognized dead ends, as well as the existence/non-existence of constant upper bounds on the difficulty of escaping local minima and benches. These distinctions divide the (set of all) planning domains into a taxonomy of classes of varying h^+ topology. As it turns out, many of the 30 investigated domains lie in classes with a relatively easy topology. Most particularly, 12 of the domains lie in classes where FF's search algorithm, provided with h^+, is a polynomial solving mechanism. We also present results relating h^+ to its approximation as implemented in FF. The behavior regarding dead ends is provably the same. We summarize the results of an empirical investigation showing that, in many domains, the topological qualities of h^+ are largely inherited by the approximation. The overall investigation gives a rare example of a successful analysis of the connections between typical-case problem structure, and search performance. The theoretical investigation also gives hints on how the topological phenomena might be automatically recognizable by domain analysis techniques. We outline some preliminary steps we made into that direction.

A stochastic local search approach to language tree reconstruction

Diachronica ◽  
2010 ◽  
Vol 27 (2) ◽  
pp. 341-358 ◽  
Author(s):  
Francesca Tria ◽  
Emanuele Caglioti ◽  
Vittorio Loreto ◽  
Andrea Pagnani
Keyword(s):  
Local Search ◽  
Phylogenetic Trees ◽  
Search Algorithm ◽  
Distance Matrix ◽  
Stochastic Local Search ◽  
Strongly Correlated ◽  
The Stability ◽  
Search Approach

In this paper we introduce a novel stochastic local search algorithm to reconstruct phylogenetic trees. We focus in particular on the reconstruction of language trees based on the comparison of the Swadesh lists of the recently compiled ASJP database. Starting from a generic tree configuration, our scheme stochastically explores the space of possible trees driven by the minimization of a pseudo-functional quantifying the violations of additivity of the distance matrix. As a consequence the resulting tree can be annotated with the values of the violations on each internal branch. The values of the deviations are strongly correlated with the stability of the internal edges; they are measured with a novel bootstrap procedure and displayed on the tree as an additional annotation. As a case study we considered the reconstruction of the Indo-European language tree. The results are quite encouraging, highlighting a potential new avenue to investigate the role of the deviations from additivity and check the reliability and consistency of the reconstructed trees.

Collision Avoidance in Multiple-Ship Situations by Distributed Local Search

2014 ◽  
Vol 18 (5) ◽  
pp. 839-848 ◽  
Author(s):  
Dong-Gyun Kim ◽  
◽  
Katsutoshi Hirayama ◽  
Gyei-Kark Park ◽  
Keyword(s):  
Local Search ◽  
Collision Avoidance ◽  
Search Algorithm ◽  
Fuzzy Theory ◽  
Search Performance ◽  
Local Search Algorithm ◽  
Collision Risk ◽  
Mode Of Transportation ◽  
Ship Collision ◽  
Approach Time

As vital transportation carriers in trade, ships have the advantage of stability, economy, and bulk capacity over airplanes, trucks, and trains. Even so, their loss and cost due to collisions and other accidents exceed those of any other mode of transportation. To prevent ship collisions many ways have been suggested, e.g., the 1972 COLREGs which is the regulation for preventing collision between ships. Technologically speaking, many related studies have been conducted. The term “Ship domain” involves that area surrounding a ship that the navigator wants to keep other ships clear of. Ship domain alone is not sufficient, however, for enabling one or more ships to simultaneously determine the collision risk for all of the ships concerned. Fuzzy theory is useful in helping ships avoid collision in that fuzzy theory may define whether collision risk is based on distance to closest point of approach, time to closest point of approach, or relative bearing – algorithms that are difficult to apply to more than one ships at one time. The main purpose of this study is thus to reduce collision risk among multiple ships using a distributed local search algorithm (DLSA). By exchanging information on, for example, next-intended courses within a certain area among ships, ships having the maximum reduction in collision risk change courses simultaneously until all ships approach a destination without collision. In this paper, we introduce distributed local search and explain how it works using examples. We conducted experiments to test distributed local search performance for certain instances of ship collision avoidance. Experiments results showed that in most cases, our proposal applies well in ship collision avoidance amongmultiple ships.

scholarly journals Planning Through Stochastic Local Search and Temporal Action Graphs in LPG

2003 ◽  
Vol 20 ◽  
pp. 239-290 ◽  
Author(s):  
A. Gerevini ◽  
A. Saetti ◽  
I. Serina
Keyword(s):  
Local Search ◽  
Search Method ◽  
Stochastic Local Search ◽  
Time System ◽  
Temporal Planning ◽  
The Core ◽  
International Planning ◽  
Domain Independent ◽  
Temporal Action

We present some techniques for planning in domains specified with the recent standard language PDDL2.1, supporting 'durative actions' and numerical quantities. These techniques are implemented in LPG, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). LPG is an incremental, any time system producing multi-criteria quality plans. The core of the system is based on a stochastic local search method and on a graph-based representation called 'Temporal Action Graphs' (TA-graphs). This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in LPG using this representation. The experimental results of the 3rd IPC, as well as further results presented in this paper, show that our techniques can be very effective. Often LPG outperforms all other fully-automated planners of the 3rd IPC in terms of speed to derive a solution, or quality of the solutions that can be produced.

scholarly journals Chaotic Search Based Equilibrium Optimizer for Dealing with Nonlinear Programming and Petrochemical Application

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 200
Author(s):  
Abd Allah A. Mousa ◽  
Mohammed A. El-Shorbagy ◽  
Ibrahim Mustafa ◽  
Hammad Alotaibi
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Phase I ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Limited Area ◽  
Search Performance ◽  
Specific Chemical ◽  
Chaotic Search

In this article, chaotic search based constrained equilibrium optimizer algorithm (CS-CEOA) is suggested by integrating a novel heuristic approach called equilibrium optimizer with a chaos theory-based local search algorithm for solving general non-linear programming. CS-CEOA is consists of two phases, the first one (phase I) aims to detect an approximate solution, avoiding being stuck in local minima. In phase II, the chaos-based search algorithm improves local search performance to obtain the best optimal solution. For every infeasible solution, repair function is implemented in a way such that, a new feasible solution is created on the line segment defined by a feasible reference point and the infeasible solution itself. Due to the fast globally converging of evolutionary algorithms and the chaotic search’s exhaustive search, CS-CEOA could locate the true optimal solution by applying an exhaustive local search for a limited area defined from Phase I. The efficiency of CS-CEOA is studied over multi-suites of benchmark problems including constrained, unconstrained, CEC’05 problems, and an application of blending four ingredients, three feed streams, one tank, and two products to create some certain products with specific chemical properties, also to satisfy the target costs. The results were compared with the standard evolutionary algorithms as PSO and GA, and many hybrid algorithms in the same simulation environment to approve its superiority of detecting the optimal solution over selected counterparts.

OHCP: A Local Search Planner Based on Ordered Hill Climbing Algorithm and Local-Minimal Restart Strategy

2011 ◽  
Vol 219-220 ◽  
pp. 1683-1688
Author(s):  
Rui Shi Liang ◽  
Hui Ma ◽  
Min Huang
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Hill Climbing ◽  
Evaluation Procedure ◽  
Heuristic Evaluation ◽  
Hill Climbing Algorithm ◽  
Novel Approach ◽  
Restart Strategy ◽  
International Planning ◽  
Planning Graph

This paper describes OHCP, a fast planner using local search based on Ordered Hill Climbing (OHC) search algorithm and local-minimal restart strategy. OHC is used as a basis of a heuristic planner in conjunction with relaxed planning graph heuristic. A novel approach is proposed in OHC framework to extract useful information from relaxed plans to preorder all promising neighborhoods, which can cut down the frequency of calling the heuristic evaluation procedure. In order to preserve completeness and improve search effort, a new restart strategy for complete search from local minimal is proposed when the local search guided by OHC fails. The ideas are implemented in our planner OHCP. Experimental results show strong performance of the proposed planner on recent international planning competition domains.

scholarly journals Improved Sparrow Search Algorithm Based on Iterative Local Search

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Shaoqiang Yan ◽  
Ping Yang ◽  
Donglin Zhu ◽  
Wanli Zheng ◽  
Fengxuan Wu
Keyword(s):  
Local Search ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Search Performance ◽  
Search Range ◽  
Variable Helix ◽  
Local Optimal Solution ◽  
Local Search Strategy

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual’s opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.

scholarly journals Enhanced Simplified Memory-bounded A Star (SMA*+)

10.29007/v7zc ◽  
2018 ◽  
Author(s):  
Justin Lovinger ◽  
Xiaoqin Zhang
Keyword(s):  
Explicit Memory ◽  
Search Algorithm ◽  
A Priori ◽  
Search Space ◽  
Search Algorithms ◽  
Search Performance ◽  
Heuristic Function ◽  
Optimal Search ◽  
Comparison Results ◽  
Memory Efficient

In 1992, Stuart Russell briefly introduced a series of memory efficient optimal search algorithms. Among which is the Simplified Memory-bounded A Star (SMA*) algorithm, unique for its explicit memory bound. Despite progress in memory efficient A Star variants, search algorithms with explicit memory bounds are absent from progress. SMA* remains the premier memory bounded optimal search algorithm. In this paper, we present an enhanced version of SMA* (SMA*+), providing a new open list, simplified implementation, and a culling heuristic function, which improves search performance through a priori knowledge of the search space. We present benchmark and comparison results with state-of-the-art optimal search algorithms, and examine the performance characteristics of SMA*+.

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