Roni Stern, Rami Puzis, Ariel Felner · Proceedings of the International Conference on Automated Planning and Scheduling · 2011
In this paper we address the following search task: find a goal with cost smaller than or equal to a given fixed constant. This task is relevant in scenarios where a fixed budget is available to execute a plan and we would like to find such a plan with minimum search effort. We introduce an algorithm called Potential search (PTS) which is specifically designed to solve this problem. PTS is a best-first search that expands nodes according to the probability that they will be part of a plan whose cost is less than or equal to the given budget. We show that it is possible to implement PTS even without explicitly calculating these probabilities, when a heuristic function and knowledge about the error of this heuristic function are given. In addition, we also show that PTS can be modified to an anytime search algorithm. Experimental results show that PTS outperforms other relevant algorithms in most cases, and is more robust.
Cited by 48Open access
Hou Xue-qing · 2011
Inspired by explosion of fireworks(bomb),a new intelligence search algorithm is proposed,which is called Explosion Search Algorithm(ESA).One theory defined as neighborhood search is proposed in ESA and the algorithm includes three important operator: explosion search operator,moving operator,mutation operator.This new algorithm has stronger ability of global search as well as local search.And it also has fast convergence and good stability features.Simulation result using standard benchmark functions and comparison with other algorithms proves the efficiency of the new algorithm.
Cited by 2
Abdul Nayeem Khalid, Naga Malleswara Rao Baki, Tarun Sai Phani Varma et al. · 2024
In the realm of combinatorial optimization, the Tabu Search algorithm has proven to be a powerful tool for solving complex problems. However, its practical implementation often encounters computational challenges, particularly in managing the frequently accessed” tabu list.” In this paper, authors introduced the Drop-set data structure, a novel approach designed to streamline the management of the tabu list within the Tabu Search algorithm. The Drop-set data structure enables the efficient addition and removal of elements from the tabu list in constant time, alleviating the computational burden associated with traditional data structures. Through experimental evaluation conducted on the well-known knapsack problem, authors demonstrated the performance enhancements achieved by integrating the Drop-set data structure into the Tabu Search algorithm. Results indicate promising reductions in computational time, thereby propelling the algorithm's convergence speed and improving solution quality. This research not only presents a practical solution to enhance Tabu Search but also establishes a foundation for the development of future optimization algorithms that can benefit from the integration of the Drop-set data structure.
Cited by 8
Yehuda Roth · Journal of Modern Physics · 2013
The collapse phenomenon, the parallelism principle and states correlation are used to define a type of a Grover rapid search engine. In our approach, the observer’s query and the Grover-unsorted-data are stored in different memories where the global state is represented by a tensor product of the associated states. In the proposed formalism, each query-state input activates an adjusted operator that implements the unsorted state in an appropriate 2-D Grover representation. It will be shown that once the representation is set, it takes mainly two operations to complete the whole query search. This seems to be a very efficient search algorithm.
Cited by 1Open access
Mohammad Dehghani, Zeinab Montazeri, O.P. Malik et al. · SHILAP Revista de lepidopterología · 2019
Random based inventive algorithms are being widely used for optimization. An important category of these algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed algorithm for optimizing the orientation game is a series of optimization algorithms that are formed with the idea of an old game and search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the game referee's orders. The best position is achieved by the laws are there in this game .In this paper, the real version of the algorithm is presented. The results of optimization of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed algorithm over the genetic algorithm and the particle swarm optimization algorithm.
Cited by 40Open access
Naik Manoj Kumar, Rutuparna Panda · 2015
Cited by 0Open access
Babak Zolghadr‐Asli · 2023
Cited by 0
Noroozi M, Mohammadi Barzelighi H, Efatinasab E et al. · Newcastle University ePrints (Newcastle Univesity)
Cited by 0Open access
Waleed Alomoush, Khairuddin Omar, Ayat Alrosan et al. · Journal of King Saud University - Computer and Information Sciences · 2018
Cited by 23Open access
Qi Song, Yourui Huang, Wenhao Lai et al. · PLoS ONE · 2021
This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.
Cited by 6Open access
Hannah B. Spitzer (14185759), Rebecca K. Meagher (14185762), Kathryn L. Proudfoot (13531738) · Figshare · 2022
Cited by 0Open access
For Dependency Analysis, Satoshi Sekine · 2000
Backward beton search ibr dependency analysis of Japanese is proposed. As dependencies hornrally go h'om left to right in Japanese, it is effective to analyze sentences backwards (from right to left). The analysis is based on a statisti- cal method and employs a beton search strategy. Based on experiments varying the beton search width, we ibund that the accnracy is not sensitive to the beam width and even the analysis with a beton width of 1 gets ahnost the stone dependency accuracy as the best accuracy using a wider beam width. This snggested a deterministic algorithm for backwards .lapanese dependency analysis, although still the beton search is effective as the N-best sentence accuracy is quite high. The time of analysis is observed to be quadratic in the sentence length.
Cited by 0
Bryar A. Hassan, Alla Ahmad Hassan, Aram M. Ahmed et al. · 2025
Evolutionary computing algorithms are one of the families of global optimization algorithms. These algorithms are problem solvers with a metaheuristic or stochastic optimization character and members of the population-based trial family. One of the recent and common population-based approaches used for mathematical problems is the Backtracking Search Optimization Algorithm (BSA). In the current chapter, a practical tutorial on BSA is provided. At first glance, evolutionary algorithms and their artificial implementation are introduced. After that, the basic steps of BSA are explained. Next, the current source code programs used to implement BSA are presented. An example explains how BSA works on a numerical optimization problem. Lastly, based on the potential of BSA for minimizing different optimization problems, a wide overview (extensive description) of the algorithm is illustrated via a general framework of BSA variants. It is intended that this chapter will offer helpful references and advice to scholars seeking to work on BSA and enhance it accordingly.
Cited by 0
José E. Gallardo (291863) · Figshare · 2015
Cited by 0Open access
Jinkun Liu · 2017
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Keshav Sharma, Pradyum Shetty, Rajith Shetty et al. · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: Apps such as Google maps have inevitably become the go-to app for travelling & we are all heavily reliant on them. But as developers can the ones starting in this field grasp the concept of these algorithms easily? Being able to use such navigational tools outdoors has been very helpful but the same cannot be applied indoors. everywhere. But understanding the core concepts & which algorithm will perform how in different situations can bring about a big change. In this project we plan to help the end-user ( developers, students ) visually study the search algorithms for the shortest distance between two points and how it is calculated by the various algorithms, thus being able to pick the best one possible. One of the main reasons to add visualizations is that we, humans, are better visual learners. Keywords: Navigation, Directional Algorithms, GUI, Shortest distance,
Cited by 0Open access
Qi Song, Yourui Huang, Wenhao Lai et al. · SHILAP Revista de lepidopterología · 2021
This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.
Cited by 0Open access
Mohammad Noroozi, Hamed Mohammadi, Emad Efatinasab et al. · IEEE Access · 2022
This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called objects, which interact with each other based on a simple mathematical model to reach the global optimum. To provide a fine balance between the explorative and exploitative behavior of a search, the proposed method utilizes a transfer operator in the adaptive step size adjustment scheme. The proposed algorithm is benchmarked with 23 unimodal, multimodal, and fixed dimensional functions and the results are verified by a comparative study with the well-known Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Tunicate Swarm Algorithm (TSA). In addition, the nonparametric Wilcoxon’s rank sum test is performed to measure the pair-wise statistical performance of the GSO and provide a valid judgment about the performance of the algorithm. The simulation results demonstrate that GSO is superior and could generate better optimal solutions when compared with other competitive algorithms.
Cited by 114Open access
Bhupendra Singh, Mohankumar Mylsamy, T. Thamaraimanalan · 2025
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Bo Xing, Wen-Jing Gao · Intelligent systems reference library · 2013
Cited by 4