To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. Nine chaotic maps reported in the literature are tested.
The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well-known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching-Learning Based Optimization. Numerical experiments using 20 benchmark functions are carried out. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. The Grey Wolf Optimizer is a nature-inspired, population-based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. (C) 2012 American Society of Civil Engineers.īio-inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. Results show that PBA performance is comparable to those of the mentioned algorithms in the benchmark functions and can be efficiently employed to solve practical FLD problem with high dimensionality. Additionally, this study compares PBA performance against bee algorithm and particle swarm optimization (PSO) performance in practical FLD problems. This study compares the performance of PBA with that of genetic algorithm (GA), differential evolution (DE), bee algorithm, and particle swarm optimization for multidimensional benchmark function problems. This study also proposes a neighborhood-windows (NW) technique for improving searching efficiency and a self-parameter-updating (SPU) technique for preventing trapping into a local optimum in high-dimensional problems. This study proposes a new optimization hybrid swarm algorithm, the particle bee algorithm (PBA), which imitates the intelligent swarming behavior of honeybees and birds. Swarm intelligence (SI), an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization, is being increasingly used to resolve various complex optimization problems.
However, FLD present a difficult combinatorial optimization problem for engineers. Facility layout design (FLD) presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance.