An improved Simulated Annealing algorithm based on ancient metallurgy techniques

Morales-Castañeda, Bernardo; Zaldívar, Daniel; Cuevas, Erik; Maciel-Castillo, Oscar; Aranguren, Itzel; Fausto, Fernando

doi:10.1016/j.asoc.2019.105761

Cited by 49 publications

(14 citation statements)

References 44 publications

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“…One such method for finding absolute target meeting tests, called simulated annealing, uses importance Monte Carlo sampling [14]. This is a stochastic method [15] that uses parameters T and E akin to temperature and energy in the Monte Carlo molecular simulations [16], [17], [18]. We propose to use this method for finding target exceeding tests.…”

Section:  mentioning

confidence: 99%

Exceeding Information Targets in Fixed-Form Test Assembly

Gospodinov

Emira

Filipov

2021

2021 International Conference Automatics and Informatics (ICAI)

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This work studies the assembly of ability estimation test forms (called tests, for short) drawn from an item bank. The goal of fixed-from test assembly is to generate a large number of different tests with information functions that meet a target information function. Thus, every test has the same ability estimation error. This work proposes a new way of test assembly, namely, drawing tests with information functions that exceed the target. This guarantees that every test has an ability estimation error that is less than the error set by the target. The work estimates the number of target exceeding tests as a function of the number of items in the test. It demonstrates that the number of target exceeding tests is far greater than the number of target meeting tests. A Monte Carlo importance sampling algorithm is proposed for target exceeding test assembly.

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Section:  mentioning

confidence: 99%

Exceeding Information Targets in Fixed-Form Test Assembly

Gospodinov

Emira

Filipov

2021

2021 International Conference Automatics and Informatics (ICAI)

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“…Simulated annealing (SA) is a single-solution-based metaheuristic search inspired by the annealing in metallurgy [26]. Due to its simplicity, less parameter, and fast convergence, SA has been widely adapted for global search and optimization during recent years [27].…”

Section: Asamentioning

confidence: 99%

Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

Jiang

Wang

Dong

et al. 2020

IEEE Internet Things J.

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An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment.Index Terms-Stacked auto encoder, deep reinforcement learning, adaptive simulated annealing, mobile edge computing.

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“…Representative bio-inspired algorithms include genetic algorithms [4], evolutionary strategies [5], differential evolution (DE) [6]- [8], spherical evolution [9], artificial immune algorithms [10], particle swarm optimization (PSO) [11], ant colony optimization [12], etc. Physics-inspired algorithms consist of simulated annealing [13], gravitational search algorithm [14], and quantum computing [15], while sociology-inspired ones usually denote imperialist competitive algorithm [16], brain storm optimization [17], culture algorithm [18], memetic algorithms [19], and so on. More importantly, these MHAs have been widely applied on various practical problems, from engineering [20], [21] to bio-informatics [22], [23], and achieved great successes in comparison with traditional mathematical analysis methods as they can obtain an acceptable solution with reasonable computational burden [24]- [28].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Yang

et al. 2021

IEEE Access

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As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

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An improved Simulated Annealing algorithm based on ancient metallurgy techniques

Cited by 49 publications

References 44 publications

Exceeding Information Targets in Fixed-Form Test Assembly

Exceeding Information Targets in Fixed-Form Test Assembly

Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

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