A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments

Vafashoar, Reza; Meybodi, Mohammad Reza

doi:10.1016/j.asoc.2019.106009

Cited by 33 publications

(6 citation statements)

References 38 publications

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“…Zhang et al (2019b) proposed a new cluster-based clonal selection algorithm, where a max-min distance cluster method based on the fitness and Euclidean distance was used to partition the population. Vafashoar and Meybodi (2020) proposed a multipopulation DE algorithm, which is different from past heterogeneous algorithms. In this proposed algorithm, each population resides in one cell of a cellular learning automaton, which is the combination of a cellular automaton with learning automata.…”

Section: (A) Multi-populationsmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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Section: (A) Multi-populationsmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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“…This is one of the studies or learning techniques that were inverted in the early 1960s that can be deliberated to be LA where it selects and inferiors their existing action per the past familiarities from the atmosphere. This makes it more suitable to tumble interested in the variety of RL in case the atmosphere is stochastically utilized; MDP is used [38] and [39].…”

Section: Learning Automatamentioning

confidence: 99%

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

Shafik

Matinkhah

Shokoor

et al. 2022

EAI Endorsed Trans IoT

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Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context. This is a comparison to supervised and non-supervised learning due to the interactive nature of the environment. Exploiting a forthcoming accumulative compensation and its stimulus of machines, complex policy decisions. The study further analyses and presents ML perspectives depicting state-of-the-art developments with advancement, relatively depicting the future trend of RL based on its applicability in technology. It's a challenge to an Internet of Things (IoT) and demonstrates what possibly can be adopted as a solution. This study presented a summarized perspective on identified arenas on the analysis of RL. The study scrutinized that a reasonable number of the techniques engrossed in alternating policy values instead of modifying other gears in an exact state of intellectual. The study presented a strong foundation for the current studies to be adopted by the researchers from different research backgrounds to develop models, and architectures that are relevant.

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“…A study of population partitioning techniques on efficiency of swarm algorithms is provided in [34]. Many nature-inspired algorithms [35]- [42] and various types of optimization algorithms [43]- [46] use static and dynamic multipopulation design to improve their efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Multisize no Migration Island-Based Differential Evolution Algorithm With Removal of Ineffective Islands

Skakovski

Jędrzejowicz

2022

IEEE Access

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The paper is a continuation of our previous research where a novel concept of multisize island model was proposed. Such multisize approach facilitates the design of island-based algorithms and brings such benefits as: improved fitness dynamics throughout the entire time of operation even without the migration of solutions between the islands. The absence of migration eliminates the need to establish the topology and the policy of migration. It also makes the efficiency of multisize island-based algorithms independent of the particular islands' size and eliminates the need of tuning the size of islands which is usually done in the case of the canonical island model. All these features indicate the superiority of the multisize island model over the canonical one. In this paper we improved earlier proposed multisize islandbased DE algorithm by adding to it the ability to automatically optimize the number of islands in operation. This feature enables the release of most computational units before the algorithm completes its operation in the case of concurrent execution of the algorithm on multiple computational units, or reduction of the algorithm running time in the case of its execution on a single computational unit. The proposed algorithm was tested by solving computationally difficult scheduling problem, which is the discrete-continuous scheduling with continuous resource discretization.INDEX TERMS Evolutionary computation, multisize island model, differential evolution, discretecontinuous scheduling.

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A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments

Cited by 33 publications

References 38 publications

A survey on evolutionary computation for complex continuous optimization

A survey on evolutionary computation for complex continuous optimization

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

A Multisize no Migration Island-Based Differential Evolution Algorithm With Removal of Ineffective Islands

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