An Introduction to Learning Automata and Optimization

Kordestani, Javidan Kazemi; Mirsaleh, Mehdi Razapoor; Rezvanian, Alireza; Meybodi, Mohammad Reza

doi:10.1007/978-3-030-76291-9_1

Cited by 4 publications

(8 citation statements)

References 228 publications

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“…The purpose of optimization in dynamic optimization problems is no longer to locate the stationary optimal solution(s) but to track the trajectories of the optimum (s) over time as accurately as possible [16][17][18]. This situation possesses additional challenges that should be addressed properly to obtain promising results.…”

Section: Optimization Challenges In Dynamic Optimization Problemsmentioning

confidence: 99%

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An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

Rezvanian¹,

Vahidipour²,

Sadollah³

2024

Optimization Algorithms - Classics and Recent Advances

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Swarm intelligence is a relatively recent approach for solving optimization problems that usually adopts the social behavior of birds and animals. The most popular class of swarm intelligence is ant colony optimization (ACO), which simulates the behavior of ants in seeking and moving food. This chapter aim to briefly overview the important role of ant colony optimization methods in solving optimization problems in time-varying and dynamic environments. To this end, we describe concisely the dynamic optimization problems, challenges, methods, benchmarks, measures, and a brief review of methodologies designed using the ACO and its variants. Finally, a short bibliometric analysis is given for the ACO and its variants for solving dynamic optimization problems.

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Section: Optimization Challenges In Dynamic Optimization Problemsmentioning

confidence: 99%

“…This is because they can compare their methods with other peer algorithms. This measure is defined as given follows [17,[29][30][31]:…”

Section: Evaluation Measuresmentioning

confidence: 99%

An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

Rezvanian¹,

Vahidipour²,

Sadollah³

2024

Optimization Algorithms - Classics and Recent Advances

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“…Learning automaton is one of the reinforcement learning techniques in artificial intelligence. Learning automata's learning ability in unknown environments is a useful technique for modeling, controlling, and solving many real problems in the distributed and decentralized environments [39]. The environment responds to the action taken in turn with a reinforcement signal.…”

Section: -Learning Automata Theorymentioning

confidence: 99%

TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

Esfandi,

Akbari Torkestani,

Karimi

et al. 2024

jist

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Due to the completely random and dynamic nature of the cloud environment, as well as the high volume of jobs, one of the significant challenges in this environment is proper online job scheduling. Most of the algorithms are presented based on heuristic and meta-heuristic approaches, which result in their inability to adapt to the dynamic nature of resources and cloud conditions. In this paper, we present a distributed online algorithm with the use of two different learning automata for each scheduler to schedule the jobs optimally. In this algorithm, the placed workload on every virtual machine is proportional to its computational capacity and changes with time based on the cloud and submitted job conditions. In proposed algorithm, two separate phases and two different LA are used to schedule jobs and allocate each job to the appropriate VM, so that a two phase adaptive algorithm based on LA is presented called TPALA. To demonstrate the effectiveness of our method, several scenarios have been simulated by CloudSim, in which several main metrics such as makespan, success rate, average waiting time, and degree of imbalance will be checked plus their comparison with other existing algorithms. The results show that TPALA performs at least 4.5% better than the closest measured algorithm.

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“…The purpose of optimization in dynamic problems has shifted from simply identifying the stationary optimal solution(s) to precisely monitoring the trajectories of the optimal solution(s) over time [12][13][14]. As a result, it is crucial to adequately tackle the extra challenges presented by this scenario to achieve promising results.…”

Section: Introductionmentioning

confidence: 99%

CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments

Rezvanian,

Vahidipour,

Saghiri

2023

Algorithms

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Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments.

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An Introduction to Learning Automata and Optimization

Cited by 4 publications

References 228 publications

An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments

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