A bottlenose dolphin optimizer: An application to solve dynamic emission economic dispatch problem in the microgrid

A novel Human Conception Optimizer for solving optimization problems

Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.

LARO: Opposition-Based Learning Boosted Artificial Rabbits-Inspired Optimization Algorithm with Lévy Flight

The artificial rabbits optimization (ARO) algorithm is a recently developed metaheuristic (MH) method motivated by the survival strategies of rabbits with bilateral symmetry in nature. Although the ARO algorithm shows competitive performance compared with popular MH algorithms, it still has poor convergence accuracy and the problem of getting stuck in local solutions. In order to eliminate the effects of these deficiencies, this paper develops an enhanced variant of ARO, called Lévy flight, and the selective opposition version of the artificial rabbit algorithm (LARO) by combining the Lévy flight and selective opposition strategies. First, a Lévy flight strategy is introduced in the random hiding phase to improve the diversity and dynamics of the population. The diverse populations deepen the global exploration process and thus improve the convergence accuracy of the algorithm. Then, ARO is improved by introducing the selective opposition strategy to enhance the tracking efficiency and prevent ARO from getting stuck in current local solutions. LARO is compared with various algorithms using 23 classical functions, IEEE CEC2017, and IEEE CEC2019 functions. When faced with three different test sets, LARO was able to perform best in 15 (65%), 11 (39%), and 6 (38%) of these functions, respectively. The practicality of LARO is also emphasized by addressing six mechanical optimization problems. The experimental results demonstrate that LARO is a competitive MH algorithm that deals with complicated optimization problems through different performance metrics.