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Advanced metaheuristic techniques for mechanical design problems
The design of complex mechanical components is a time-consuming process which involves
many design variables with multiple interacted objectives and constraints. Traditionally, the …
many design variables with multiple interacted objectives and constraints. Traditionally, the …
Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study
Multiobjective reliability-based design optimization (RBDO) is a research area, which has
not been investigated in the literatures comparing with single-objective RBDO. This work …
not been investigated in the literatures comparing with single-objective RBDO. This work …
Boosting whale optimization with evolution strategy and Gaussian random walks: An image segmentation method
Stochastic optimization has been found in many applications, especially for several local
optima problems, because of their ability to explore and exploit various zones of the feature …
optima problems, because of their ability to explore and exploit various zones of the feature …
Deep learning for detecting distresses in buildings and pavements: a critical gap analysis
Purpose The massive number of pavements and buildings coupled with the limited
inspection resources, both monetary and human, to detect distresses and recommend …
inspection resources, both monetary and human, to detect distresses and recommend …
Optimization of truss structures using multi-objective cheetah optimizer
In this study, a multi-objective version of the recently proposed cheetah optimizer called
multi-objective cheetah optimizer (MOCO) has been proposed. MOCO draws inspiration …
multi-objective cheetah optimizer (MOCO) has been proposed. MOCO draws inspiration …
A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints
Constrained multi-objective optimization problems (CMOPs) are difficult to handle because
objectives and constraints need to be considered simultaneously, especially when the …
objectives and constraints need to be considered simultaneously, especially when the …
Hybrid filter-wrapper feature selection using whale optimization algorithm: A multi-objective approach
Feature selection aims at finding the minimum number of features that result in high
classification accuracy. Accordingly, the feature selection is considered as a multi-objective …
classification accuracy. Accordingly, the feature selection is considered as a multi-objective …
Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models
L Peng, C He, AA Heidari, Q Zhang, H Chen… - Energy Conversion and …, 2022 - Elsevier
With the recent emphasis on new energy sources, solar photovoltaic cells have received
widespread attention from scholars as a highly efficient and clean new energy source …
widespread attention from scholars as a highly efficient and clean new energy source …
MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems
To handle the multiobjective optimization problems of truss-bar design, this paper introduces
a new metaheuristic multiobjective optimization algorithm. The proposed algorithm is based …
a new metaheuristic multiobjective optimization algorithm. The proposed algorithm is based …
A multi-objective chaos game optimization algorithm based on decomposition and random learning mechanisms for numerical optimization
Abstract Chaos Game Optimization (CGO) is a heuristic optimization approach that
estimates global optima for optimization problems using operators based on chaos theory …
estimates global optima for optimization problems using operators based on chaos theory …