In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm c...

This chapter presents a study about the behavior of Particle Swarm Optimization (PSO) in constrained search spaces. A comparison of four well-known PSO variants used to solve a set of test problems is presented. Based on the information obtained, the most competitive PSO variant is detected. From this preliminary analysis, the performance of this variant is improved with two simple modification...

Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only ...

Multilevel thresholding is one of the most popular image segmentation techniques. This paper presents a new multilevel maximum entropy thresholding method based on modified seeker optimization (MSO) algorithm. In the proposed method the thresholding problem is treated as an optimization problem and solved by using the MSO metaheuristics. Particle swarm optimization (PSO) algorithm is also imple...

The reliability design is related to the performance analysis of engineering systems. The reliability redundancy optimization problems involve selection of components with multiple choices and redundancy levels that produce maximum benefits, can be subject to the cost, weight, volume and reliability constraints. Classical mathematical methods fail in handling non-convexities and nonsmoothness i...

-A novel approach for the implementation of Nonlinear Model Predictive Control (NMPC) using Particle Swarm Optimization (PSO) technique is proposed. Two different approaches are made in the PSO algorithms, Random PSO (RPSO) and knowledge based PSO (KPSO) for the determination of optimum controller gain in MPC structure In order to test the performance of the proposed PSO based MPC system a nonl...

Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. This paper proposes a Multi Swarm Particle Swarm Optimization (MS-PSO) algorithm inspired by the animal collective behavior, the movement of the swarm and the intelligence of the ...

Together with the increase in electronic circuit complexity, the design and optimization processes have to be automated with high accuracy. Predicting and improving the design quality in terms of performance, robustness and cost is the central concern of electronic design automation. Generally, optimization is a very difficult and time consuming task including many conflicting criteria and a wi...

Graph coloring problem (GCP) is one of the most studied combinatorial optimization problems. It is important in theory and practice. There have been many algorithms for graph coloring problem, including exact algorithms and (meta-)heuristic algorithms. In this paper, we attempt another meta-heuristic method⎯particle swarm optimization to graph coloring problem. Particle swarm optimization (PSO)...

This paper describes the application of Particle Swarm Optimization (PSO) for gait optimization on a humanoid robot. The biped gait is modeled by a number of parameterizable trajectories. To achieve omni-directional walking, different sets of gait parameters are optimized for specific walk directions and interpolated later. By using a fitness test based on an acceleration walk, the optimized se...