Abstract The differential evolution (DE) algorithm is an efficient random search based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong ability and good robustness. However, performance DE very sensitive to design different operators setting control parameters. To solve these key problems, this paper proposes improve...

Energy is one of the most fundamental elements today’s economy. It becoming more important day by with technological developments. In order to plan energy policies countries and prevent climate change crisis, CO2 emissions must be under control. For this reason, estimation has become an factor for researchers scientists. study, a new hybrid method was developed using optimization methods. The S...

Swarm control has become a challenging topic for the current unmanned aerial vehicle (UAV) swarm due to its conflicting individual behaviors and high external interference. However, in contrast static obstacles, limited attention been paid fission–fusion behavior of against dynamic obstacles. In this paper, inspired by interaction mechanism motion starlings, we propose Bio-inspired Self-organiz...

Recently, classifier ensemble methods are gaining more and more attention in the machine-learning and data-mining communities. In most cases, the performance of an ensemble is better than a single classifier. Many methods for creating diverse classifiers were developed during the past decade. When these diverse classifiers are generated, it is important to select the proper base classifier to j...

This paper presents a method to employ particle swarms optimizers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each which is optimized cooperatively in its own swarm. The application of this technique to neural network training is investigated, with promising results.

The parameter identification problem can be modeled as a non-linear optimization problem. In this problem, some unknown parameters of a mathematical model presented by an ordinary differential equation using some experimental data must be estimated. This paper presents a shuffled frog leaping algorithm for solving parameter identification problem. An opposition-based initialization strategy is ...

Language models that use interleaving, or shuffle, operators have applications in various areas of computer science, including system verification, plan recognition, and natural language processing. We study the complexity of the membership problem for such models, i.e., how difficult it is to determine if a string belongs to a language or not. In particular, we investigate how interleaving can...

The current paper uses a scenario from logistics to show that modern heuristics, and in particular particle swarm optimization (PSO) can significantly add to the improvement of staff scheduling in practice. Rapid, sub-daily planning, which is the focus of our research offers considerable productivity reserves for companies but also creates complex challenges for the planning software.

This study is investigated the optimum parameters for a tuned mass damper (TMD) under the seismic excitation. Shuffled complex evolution (SCE) is a meta-heuristic optimization method which is used to find the optimum damping and tuning frequency ratio for a TMD. The efficiency of the TMD is evaluated by decreasing the structural displacement dynamic magnification factor (DDMF) and acceleration ...