Demosthenes Ellinas, Christos Konstadakis · ArXiv.org · 2001
External environment influences on Grover's search algorithm modeled by quantum noise are investigated. The algorithm is shown to be robust under that external dissipation. Explicitly we prove that the resulting search positive maps acting on unsorted N-dimensional database made of projective density matrices depend on x the strength of the environment, and that there are infinitely many x values for which search is successful after O(\sqrt{N}) queries. These algorithms are quantum entropy increasing.
Cited by 1Open access
Karem A. Sakallah · 1999
This paper introduces GRASP (Generic seaRch Algorithm for the Satisfiability Problem), a new search algorithm for Propositional Satisfiability (SAT). GRASP incorporates several search-pruning techniques that proved to be quite powerful on a wide variety of SAT problems. Some of these techniques are specific to SAT, whereas others are similar in spirit to approaches in other fields of Artificial Intelligence. GRASP is premised on the inevitability of conflicts during the search and its most distinguishing feature is the augmentation of basic backtracking search with a powerful conflict analysis procedure. Analyzing conflicts to determine their causes enables GRASP to backtrack nonchronologically to earlier levels in the search tree, potentially pruning large portions of the search space. In addition, by recording the causes of conflicts, GRASP can recognize and preempt the occurrence of similar conflicts later on in the search. Finally, straightforward bookkeeping of the causality chains leading up to conflicts allows GRASP to identify assignments that are necessary for a solution to be found. Experimental results obtained from a large number of benchmarks indicate that application of the proposed conflict analysis techniques to SAT algorithms can be extremely effective for a large number of representative classes of SAT instances.
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Fouad, Ahmed · 2024
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Zhang, Shangshan · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) · 2014
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Suvarna Pansambal, R. Udayakumar, Aboul Sayed et al. · International Journal of Recent Technology and Engineering (IJRTE) · 2019
Researchers’ are taking keen interest in Optimization algorithms due to their heuristic and meta-heuristic nature. Heuristic algorithms find the arrangement by utilizing the experimentation strategy. Then again, meta-heuristic algorithms discover the response at a more elevated tier. Several nature-based metaheuristic algorithms are easily accessible. Askarzadeh has introduced the Crow search algorithm and stated that it is meta-heuristic optimization algorithm. The astute conduct of the crow moves CSA. Crows are keen on putting away the abundance nourishment at concealing spots and recuperating it at whatever point it is needed. CSA's previous outcomes show that it can unravel different complex building related optimization issues. There are six compelled building plan issues, and CSA is utilized to upgrade these issues. This paper focuses on a far-reaching investigation of CSA in the diverse application is given with the examination just as the exhibitions of the CSA in the different structure is talked about.
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Hennadii Kovalov · Zenodo (CERN European Organization for Nuclear Research) · 2026
Problem statement We have two objects (proton, hafnium sample, macroscopic resonators) with their own chronal rhythms τ₁ and τ₂. Their state is described by a complex amplitude: Ψ₁ = A₁ exp(iφ₁),Ψ₂ = A₂ exp(iφ₂) where phase φ = ∫ (1/τ) dt is the accumulated chronal angle. Objective: to find such external influences (frequency, phase, duration) that |φ₁ - φ₂| → 0 with a given accuracy.
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Sukumaran, Vandana · Zenodo (CERN European Organization for Nuclear Research) · 2025
This dataset contains the processed data for Sukumaran, V. and Vishwakarma, B.D., Accounting for shelf width in selecting altimetry observations for coastal sea level variability improves its agreement with tide gauges. Code is available in the GitHub repository:All codes are uploaded in Github repository https://github.com/Vandana1195/Dynamic-Search-Radius.
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Youssef Baddi, Mohamed Dafirech-cherif, El Kettani · 2016
Abstract:Within the development of network multimedia technology, more and more real-time multimedia applications arrive with the need to transmit information using multicast communication. Multicast IP routing is an important topic, covering both theoretical and practical interest in different networks layers. In network layer, there are several multicast routing protocols using multicast routing trees different in the literature. However PIM-SM and CBT protocols remain the most used multicast routing protocols; they propose to use a shared Core-based Tree CBT. This kind of tree provides efficient management of multicast path in changing group memberships, scalability and performance. The main problem concerning the construction of a shared tree is to determine the best position of the core. QoS-CS’s problem (QoS constraints core Selection) consists in choosing an optimal multicast router in the network as core of the Shared multicast Tree (CBT), within specified associated QoS constraints. The choice of this specific router, called RP in PIM-SM protocol and core in CBT protocol, affects the structure of multicast routing tree, and therefore influences performances of both multicast session and routing scheme. QoS-CS is an NP complete problem which needs to be solved through a heuristic algorithm: in this paper, we propose a new core Selection algorithm based on Variable Neighborhood Search algorithm and a new CMP fitness function. Simulation results show that good performance is achieved in multicast cost, end-to-end delay, tree construction delay and others metrics.
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Nor Azlina Bt Ab. Aziz, Zuwairie Ibrahim, Sophan Wahyudi Nawawi et al. · Frontiers in artificial intelligence and applications · 2014
Gravitational search algorithm (GSA) is a new member of swarm intelligence algorithms. It stems from Newtonian law of gravity and motion. The performance of synchronous GSA (S-GSA) and asynchronous GSA (A-GSA) is studied here using statistical analysis. The agents in S-GSA are updated synchronously, where the whole population is updated after each member's performance is evaluated. On the other hand, an agent in A-GSA is updated immediately after its performance evaluation. Hence an agent in A-GSA is updated without the need to synchronize with the entire population. Asynchronous update is more attractive from the perspective of parallelization. The results show that both implementations have similar performance.
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Ezzahir, Redouane, Bessiere, Christian, Belaissaoui, Mustapha et al. · 2007
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Milad Abolhasani Siahkali, Hamid Mirvaziri, Abbas Bahrololoum · Research Square · 2022
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Jifan Cui · 2024
Crayfish optimization algorithm (COA) is a swarm-based metaheuristic algorithm proposed in July 2023. The fact that the optimal individuals are not processed makes the COA algorithm less capable of exploring in the early optimization phase and converging in the later phase. In this paper, an improved COA algorithm based on non-monopoly search called NOCOA was proposed. The optimal individuals are perturbed using a non-monopoly search strategy, and the strategy is further optimized using Gaussian and Cauchy operators, which enhances the algorithm optimization ability. Finally, the superior performance of NOCOA is verified by the CEC2017 test suite.
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Ulder, NLJ Nico · TU/e Research Portal (Eindhoven University of Technology) · 1990
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Malik Braik, Mohammed A. Awadallah, Mohammed Azmi Al‐Betar et al. · Soft Computing · 2023
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Satoshi Ono, Yusuke Hirotani, Shigeru Nakayama · 2007
In recent years, many evolutionary computation methods have been proposed and applied to real-world problems. However gradient methods are still promising in problems involving real-coded parameters. In addition, it is desirable to find not only an optimal solution but also several quasi-optimal solutions in most real-world problems. Although some methods aiming at searching for multiple solutions like genetic algorithm with sharing (GAS) and immune algorithm (IA) have been proposed, they could not find highly qualified solution in real-coded problems. This paper proposes a hybrid algorithm of real-coded IA and quasi-Newton method for multiple solution search in multimodal optimization problems. Experimental results have shown that the proposed algorithm can find optimal and quasi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal benchmark functions.
Cited by 1
Malik Braik, Mohammad I. Awadallah, Mohammed Azmi Al‐Betar et al. · Research Square · 2022
Abstract Economic Load Dispatch (ELD) is an important task in power systems that is concerned with scheduling a set of thermal generating units to produce specific power at low consumption costs. In this task, there are some operational constraints related to power demand, power output, valve-point loading effect, transmission losses, prohibited operating zones, and ramp-rate limits. ELD is drafted as a non-linear, non-convex, and highly constrained optimization problem with a large number of local minima. Due to the complex and rugged search space of this problem, it has been tackled by many meta-heuristic algorithms. To cope with the above issues of this problem, we presented a novel Improved based Hybrid Capuchin Search Algorithm (IHCSA). First, a memory element was added to CSA to consolidate its position and velocity updating mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive functions rather than stationary social and cognitive parameters were used to manage and adequately balance exploration and exploitation of features over the iterations path. Finally, the hybridization was carried out using Gradient Search Algorithm (GBO) and Local Escaping Capability (LEO) to strengthen the intensification ability of CSA and balance its search capabilities to fulfill robust search performance. The proficiency of IHCSA was divulged by assessing it on a computationally difficult ELD problem under 6 different tests with different numbers of generators and loading conditions. The proposed IHCSA provided the best performance, in terms of the minimum cost, among the basic CSA and other optimization methods.
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Joong Hoon Kim · Procedia Engineering · 2016
Since the Harmony Search Algorithm (HSA) was first introduced in 2001, it has drawn a world-wide attention mainly because of its balanced combination of exploration and exploitation and ease of application. The HSA, inspired by musical performance process, consists of three operators: random search, harmony memory considering rule, and pitch adjusting rule. The ways of handling exploration and exploitation with the three operators make the HSA a unique metaheuristic algorithm. However, a series of papers was recently published by an author which insisted that the HSA is equivalent to an evolution strategy (ES). The ES, based on ideas of adaptation and evolution, consists of two operators: recombination and mutation operators. Except the similarity in generating a single new solution at each iteration which can replace the worst solution in the population, other components (e.g., their exploration and exploitation strategies and structure) are totally different between the HSA and ES. This paper is written to rebut and point out academic flaws in the papers.
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Georgios N. Kouziokas · 2023
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Dhananjay P Mehendale · 2025
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Zou De-xuan · 2010
For the purpose of avoiding the disadvantage of improved harmony search (IHS) algorithm,an adaptive harmony search-particle swarm optimization (AHSPSO) algorithm is presented. In the harmony memory of AHSPSO,every variable is updated by using the PSO algorithm,then adjusted by adaptive parameters PAR and bw. So this algorithm can improve the search efficiency of multidimension problems. Finally,the proposed AHSPSO algorithm is tested and contrasted with IHS,PSO,and SA. The test results show the favorable abilities of accuracy and escaping local minimums.
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