A Novel Hunting based Algorithm: Chimp Optimization Algorithm (ChOA) Utilizing to tackle different Optimization problems in different Industrial tasks.

Join 372 other subscribers

1. Introduction

The chimpanzee, also known as the common chimpanzee, robust chimpanzee, or simply “chimp”, is a species of native to the forests of tropical Africa [1]. The chimpanzee is covered in coarse black hair, but has a bare face, fingers, toes, palms of the hands, and soles of the feet. They are as much as the closest to the humans living relatives the relationship between the chimp and the human DNA are so similar because they are descended from a single ancestor species of Hominoid that lived seven or eight million years ago. In the Chimp Optimization Algorithm (ChOA) which imitate the social diversity and hunting behavior of chimps [2]. The hunting behaviors of chimps are classified into driving, chasing, blocking, and attacking [3]. The comparative results indicated that the ChOA is able to solve real-world optimization problems with unknown search spaces.

2. Inspiration of ChOA

A novel hunting-based optimization algorithm called Chimp Optimization Algorithm (ChOA) which is inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. In a chimp colony, there are four types of chimps entitled for the hunting process known as driver, barrier, chaser, and attackers [4]. They all have different abilities, but these diversities are necessary for a successful hunt. The role of each chimp in the hunting to attack the prey is categorized as below;

  • Driver: The role of drivers is to follows the prey without attempting to catch up with it.
  • Barriers: The role of barriers is stationed at the bottom of the trees and climb up to block prey that takes off in a different direction.
  • Chasers: The role of chasers is move rapidly after the prey to catch up with it.
  • Attackers: Finally the attackers prognosticate the breakout route of the prey to infliction it to the prey back towards the chasers or down into the lower canopy.

Male chimps hunt more than females. When caught and killed, the meal is distributed to all hunting party members and even bystanders [5]. Attackers are thought to need much more cognitive endeavor in prognosticating the subsequent movements of the prey, and they are thus remunerated with a larger piece of meat after a successful hunt [6].

Fig 1: Inspiration of ChOA

3. Relationship between chimp and human DNA

Chimpanzees are genetically closest to humans, and in fact, chimpanzees share about 98.6% ofour DNA [7]. Human share more of our DNA with chimpanzees than with monkeys or other groups. Ever since researchers sequenced the chimp genome, they have known that humans share about 99% of our DNA with chimpanzees, making them our closest living relatives.

Fig 2: Relationship between chimp and human DNA

4. Position updating Machanism

The process of position updating Machanism is the search chimp’s location in the search space regarding the position of other chimp positions [8]. Then, the final position is located randomly in a circle which is defined by attacker, barrier, chaser and driver chimp positions. That is, the prey position is estimated by four best groups and other chimps randomly update their positions within it [9].

Fig 3: Position Updating Machanism

5. Numerical Implementation of CHOA

In this section, mathematical models of group, driving, blocking, chasing and attacking are derived.

To mathematically the driving and chasing the prey, is represented by the equations,

Where n indicates the number of current iteration, c, m, and x are the coefficient vectors,   are the vector of prey position and  are the position vector of a chimp.

The vectors c, m, x are calculated by the equations as follows,

Where  and  are the random vectors in the range of [0, 1]. Finally, m is a chaotic vector calculated based on various chaotic map so that this vector represents the effect of the sexual motivation of chimps in the hunting process [10].

In order to mathematically implement the behavior of the chimps, it is assumed that the first best solution available by the attacker, driver, barrier and chaser are better informed about the location of potential prey [11]. So, four of the best solutions yet obtained is stored and other chimps are forced to update their positions according to the best chimps locations. This relationship is expressed by the following equations,

When the random values is lie in the range of [-1, 1], the next position of a chimp can be in any location between its current position and the position of the prey.

Form the overall equations,

The normal updating position mechanism or the chaotic model to update the position of chimps during optimization. The mathematical model is expressed by

Where is a random number in [0, 1]. 

6. Pseudo code of ChOA

Fig 4: Pseudo code of ChOA

7. Flowchart of ChOA

Fig 5: Flowchart of ChOA

8. Advantages & Disadvantages of ChOA

Fig 6: Advantages & Disadvantages of ChOA

9. Applications of ChOA

The ChOA algorithm can be applied to various kind of engineering optimization problems such as,

  • Engineering Design [12].
  • Neutral Networks [13].
  • Data Acquisition [14].
  • Computer Analyzing [15].
Fig 7: Applications of ChOA

Reference

[1]  Khishe M, Mosavi M (2020) Chimp optimization algorithm. Expert Systems with Applications 149:113338. doi: 10.1016/j.eswa.2020.113338

[2] Khabbazi A, Gargari E, Lucas C (2009) Imperialist competitive algorithm for minimum bit error rate beamforming. International Journal of Bio-Inspired Computation 1:125. doi: 10.1504/ijbic.2009.022781

[3]. Beyer H, Schwefel H (2002) Journal search results – Cite This for Me. Natural Computing 1:3-52. doi: 10.1023/a: 1015059928466

[4]. Boesch C (2002) Cooperative hunting roles among taï chimpanzees. Human Nature 13:27-46. doi: 10.1007/s12110-002-1013-6

[5]. Couzin I, Laidre M (2009) Fission–fusion populations. Current Biology 19:R633-R635. doi: 10.1016/j.cub.2009.05.034

[6]. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1:3-18. doi: 10.1016/j.swevo.2011.02.002

[7]. Faris H, Mafarja M, Heidari A et al. (2018) An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems 154:43-67. doi: 10.1016/j.knosys.2018.05.009

[8]  Heidari A, Pahlavani P (2017) an efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Applied Soft Computing 60:115-134. doi: 10.1016/j.asoc.2017.06.044

[9] Kaveh M, Khishe M, Mosavi M (2018) Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integrated Circuits and Signal Processing 100:405-428. doi: 10.1007/s10470-018-1366-3

[10]  Khishe M, Safari A (2019) Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm. Wireless Personal Communications 108:2241-2260. doi: 10.1007/s11277-019-06520-w

[11]  Mafarja M, Mirjalili S (2018) Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Computing 23:6249-6265. doi: 10.1007/s00500-018-3282-y

[12] Khishe M, Mosavi M, Moridi A (2018) Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation. Applied Acoustics 137:121-139. doi: 10.1016/j.apacoust.2018.03.012

[13] Pijarski P, Kacejko P (2019) A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization 51:2049-2068. Doi: 10.1080/0305215x.2019.1565282

[14] Pijarski P, Kacejko P (2019) A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization 51:2049-2068. doi: 10.1080/0305215x.2019.1565282

[15]  Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1:67-82. doi: 10.1109/4235.585893

Join 372 other subscribers

Leave a Reply

%d bloggers like this: