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Solving multiobjective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm
The job-shop scheduling problem (JSP) is NP hard, which has very important practical
significance. Because of many uncontrollable factors, such as machine delay or human …
significance. Because of many uncontrollable factors, such as machine delay or human …
The benefits of population diversity in evolutionary algorithms: a survey of rigorous runtime analyses
D Sudholt - … of evolutionary computation: Recent developments in …, 2019 - Springer
Population diversity is crucial in evolutionary algorithms to enable global exploration and to
avoid poor performance due to premature convergence. This chapter reviews runtime …
avoid poor performance due to premature convergence. This chapter reviews runtime …
Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors
In today's competitive environment, it is essential to design a flexible-responsive
manufacturing system with automatic material handling systems. In this article, a fuzzy mixed …
manufacturing system with automatic material handling systems. In this article, a fuzzy mixed …
Multiobjective evolution of the explainable fuzzy rough neural network with gene expression programming
B Cao, J Zhao, X Liu, J Arabas… - … on Fuzzy Systems, 2022 - ieeexplore.ieee.org
The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input
membership degrees, which lacks expressiveness and flexibility. In this article, a novel …
membership degrees, which lacks expressiveness and flexibility. In this article, a novel …
A proof that using crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation
Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called
Pareto optimisation) as they use a population to store trade-offs between different objectives …
Pareto optimisation) as they use a population to store trade-offs between different objectives …
Cuckoo search algorithm with fuzzy logic and Gauss–Cauchy for minimizing localization error of WSN
X Ou, M Wu, Y Pu, B Tu, G Zhang, Z Xu - Applied Soft Computing, 2022 - Elsevier
The location of the sensor node is critical in wireless sensor networks (WSN) as the
information acquired by the sensor node may be worthless without knowing its source …
information acquired by the sensor node may be worthless without knowing its source …
Evolutionary algorithms for parameter optimization—thirty years later
Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …
developments in the field of evolutionary algorithms, with applications in parameter …
Analysing the robustness of NSGA-II under noise
Runtime analysis has produced many results on the efficiency of simple evolutionary
algorithms like the (1+ 1) EA, and its analogue called GSEMO in evolutionary multiobjective …
algorithms like the (1+ 1) EA, and its analogue called GSEMO in evolutionary multiobjective …
Intelligent outage probability prediction for mobile IoT networks based on an IGWO-elman neural network
L Xu, X Yu, TA Gulliver - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
Advances in sensor technology have accelerated the development of mobile internet
applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile …
applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile …
Spherical search algorithm with adaptive population control for global continuous optimization problems
Spherical search algorithm (SSA) calculates the spherical boundary and generates new
solutions on it by two sub-populations jointly. Many researches have shown that SSA is a …
solutions on it by two sub-populations jointly. Many researches have shown that SSA is a …