A novel Human Conception Optimizer for solving optimization problems

Abstract

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.

An Improved DV-Hop Localization Algorithm Based on Human Conception Optimization with Time Varying Acceleration Coefficients for Wireless Sensor Network

“…Human Conception Optimization (HCO), is one of the optimization methods that was recently introduced [1]. Prior to estimating the distance between the unknown nodes and anchor nodes using a mathematical model, the optimisation model may be applied in HCO to give a solution.…”

“…All details on the hop counts and the distance values between the anchor nodes (A 1 , A 2 and A 3 ) and the unknown nodes are being given in Figure 1. The standard distance may now be calculated using (1). Let's take the unknown node to be U n and calculate the distance between the anchor nodes to the unknown node using (2).…”

An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks using Variable Velocity Strategy Human Conception Optimization

“…The Human Conception Optimization (HCO) approach is a novel optimization methodology [2]. The optimization model can be used in HCO to estimate the separation among the unidentified and the beacon nodes before employing an analytical framework.…”

Swarm Magnetic Optimizer: A New Optimizer that Adopts Magnetic Behaviour

“…Metaheuristics is a well-known stochastic method for solving various optimization issues. Its popularity is a result of its advantage in locating viable solutions to various optimization problems by utilizing possible computation resources due to its stochastic approach but at the expense of a guarantee of locating the genuine optimality [1]. In every optimization issue, multiple potential solutions must be found [2]; hence, tracing all these potential solutions requires an exorbitant number of computational resources, making the problem nearly challenging to solve.…”