Development Of Metaheuristic Algorithms Research Articles

Presenting a mathematical model of blood supply chain considering the efficiency of collection centers and development of metaheuristic algorithm in M/M/C/K queuing system.

In this study, a multiobjective model was devoted to the objectives of minimizing blood supply chain costs and minimizing the waiting time of blood donors for blood transfusion and minimizing blood transfusion schedule and increasing the efficiency of fixed and mobile centers in collecting blood. One of the most important constraints considered in the mathematical model is the capacity constraints of considering fixed and mobile blood facilities and management of the transfer of blood products to centers for collecting and distinguishing healthy and unhealthy blood. A multiobjective model was considered with the objectives of minimizing blood supply chain costs, the waiting time of blood donors for blood transfusion, and blood transfusion timing and increasing the efficiency of fixed and mobile centers in blood collection. The model findings were analyzed in order to validate the model on a larger scale, using the meta-innovative algorithm NSGAII and MOSPO. According to the research findings, we suggest that fuzzy uncertainty and fair distribution problem shouldn't be added to the dimensions of the main problem, and further analysis should be done in this area. It was shown that the NSGAII algorithm's performance was better than the MOPSO meta-heuristic algorithm.

A new K-means grey wolf algorithm for engineering problems

PurposeThis paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves.Design/methodology/approachThe development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO).FindingsResults illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions.Originality/valueResults prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.

Effects of objective function in PID controller design for an AVR system

Regulation capability of an automatic voltage regulator (AVR) system still needs to be improved to keep the output voltage of the generator within the AVR system at the desired level. Researchers have been developing new control structures and designing controllers to improve the performance of the AVR system. Designing of PID controller, which is commonly preferred controller due to its simple structure and robustness against to system parameter changes, has an important place among these studies. Especially with the development of metaheuristic algorithms, more successful PID controller designs are emerging by using these algorithms than traditional design methods. Undoubtedly, the objective function utilized also has a significant effect on this success. Therefore, effects of the objective function in PID controller design process for an AVR system are examined in this study. Two different PID controllers are designed using two different metaheuristic algorithms, namely, crow search algorithm (CSA) and ant colony optimization (ACO) algorithm. The parameters of the PID controllers are optimally tuned by using five different objective functions in both algorithms. These objective functions are: Integral of absolute error (IAE), integral of squared error (ISE), integral of time-weighted absolute error (ITAE), integral of time-weighted squared error (ITSE), and a commonly used user-defined objective function. The performance of the designed PID controllers are compared in terms of transient response characteristics and performance metrics. In addition, in order to evaluate the stability of the AVR system with the designed controllers, bode analysis, pole-zero map analysis and robustness analysis are performed.