optimizationswarm intelligenceSwarm-based optimization algorithms have prevailed in the field of metaheuristics for the past decades. With their application field spanning from combinatorial problems to continuous and mixed integer problems, swarm-based algorithms are currently part of the state-of-the-art...
Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by ...
While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In...
Salp swarm algorithm (SSA) is a unique swarm intelligent algorithm widely used for various practical applications due to its simple framework and good optimization performance. However, like other swarm-based algorithms, SSA yields local optimal solutions and has a slow convergence rate and low soluti...
In this study, well-known evolutionary and swarm-based optimization algorithms are compared while solving the multilevel color image thresholding problem. As the objective function, the Kapur's maximum entropy method is used. Evolutionary algorithms used in the study are Evolution Strategy (ES), Gene...
Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by ...
Table4shows the parameters set for each of the optimization algorithms used for comparison. All of the parameters hold their usual meaning, as referred to in the original papers cited. The same number of population and iterations were used throughout for all the OAs to maintain consistency. Tabl...
The results show that the PSO with mutation algorithm is significantly better than other PSO-based algorithms because it can overcome the drawback of trapping in the local optimum points and obtain better inverse solutions. The effects of measurement errors, number of dimensionalities, and number ...
Our experience is that traditional design algorithms tend to have problems finding optimal designs when there are several variables to optimize. They are likely to stall at a local optimum or break down because of the huge computational burden when there are many variables to optimize. Several ...
Firefly algorithms for multimodal optimization. In Proceedings of the 5th international conference on stochastic algorithms: Foundations and applications (pp. 169–178). Berlin: Springer. Yang, X. (2010). Engineering optimization: An introduction with metaheuristic applications. Hoboken, NJ: Wiley. Book...