Ciarleglio, M.; 2007: Modular Abstract Self-Learning Tabu Search (MASTS) Metaheuristic Search Theory and Practice: The University of Texas at Austin; 207p.Ciarleglio, M.: Modular Abstract Self-Learning Tabu Search (MASTS) Metaheuris- tic Search Theory and Practice. PhD thesis, Univ. Texas...
The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide
This research aimed to investigate the effectiveness of using physics-based metaheuristic algorithms in combination with ensemble machine-learning models for landslide susceptibility mapping (LSM). By optimizing two ensemble machine learning models (Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)...
Nowadays, metaheuristic algorithms have been utilized for the optimization of intricate real-world scientific and engineering problems; since they do not need complicated mathematical expressions, they can deal with constraints much more simply than traditional methods, and they all try to favour the sea...
and those with simple or complex dynamics32,33. Due to the use of various linear models to represent the different industrial processes, several MPC control algorithms have been approved, namely the Model Algorithmic Control (MAC) proposed by34, the Dynamic Matrix Control (DMC) suggested by35, ...
This survey would invariably provide the much needed guidance to future enthusiastic researchers and industrial practitioners alike, who have interest in adapting the nature-inspired metaheuristic algorithms to solve the well-known BPP. Specifically, we may summarize the technical contributions of this ...
In practice, projects may contain many activities. To schedule such projects, under constraints of limited resource and precedence relations, it becomes an NP hard problem. Any exact algorithms will have difficulty solving such problems. In addition, many activities of a project are quite often impr...
2015, Simulation Modelling Practice and Theory Citation Excerpt : Simulation optimization outperforms deterministic exact procedures in terms of considering stochastic and detailed production behaviour. Moreover, simulation optimization considers the utilization of machines, buffer, and cycle time [1]. The ...
problems where they have a great number of local-optimal solutions and a non-convex search space, making it really difficult for optimization algorithms to conduct a search procedure in such problems2. Efficient design of truss structures considering truss layout optimum design is a known technique ...
theory. Differential Evolution (DE) algorithm is another well-known stochastic population-based algorithm for global optimization2,3. Furthermore, with the popularity of the Imperialist Competitive Algorithm4, there have been various more frequently used population-based algorithms, including Charged System...