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A new bio-inspired algorithm Chicken swarm optimization.pdf 214KB

Y. Tan et al. (Eds.): ICSI 2014, Part I, LNCS 8794, pp. 86–94, 2014.

A New Bio-inspired Algorithm:

Chicken Swarm Optimization

Xianbing Meng

1,2

, Yu Liu

, Xiaozhi Gao

1,3

, and Hengzhen Zhang

College of Information Engineering, Shanghai Maritime University,

1550 Haigang Avenue, Shanghai, 201306, P.R. China

Chengdu Green Energy and Green Manufacturing R&D Center,

355 Tengfei Road No. 2, Chengdu, 610200, P.R. China

Department of Electrical Engineering and Automation, Aalto University School

of Electrical Engineering, Otaniementie 17, FI-00076 Aalto, Finland

x.b.meng12@gmail.com, yu.liu@vip.163.com

Abstract. A new bio-inspired algorithm, Chicken Swarm Optimization (CSO),

is proposed for optimization applications. Mimicking the hierarchal order in the

chicken swarm and the behaviors of the chicken swarm, including roosters,

hens and chicks, CSO can efficiently extract the chickens’ swarm intelligence

to optimize problems. Experiments on twelve benchmark problems and a speed

reducer design were conducted to compare the performance of CSO with that of

other algorithms. The results show that CSO can achieve good optimization re-

sults in terms of both optimization accuracy and robustness. Future researches

about CSO are finally suggested.

Keywords: Hierarchal order, Chickens’ behaviors, Swarm intelligence, Chick-

en Swarm Optimization, Optimization applications.

1 Introduction

Bio-inspired meta-heuristic algorithms have shown proficiency of solving a great

many optimization applications [1, 2]. They exploit the tolerance for imprecision and

uncertainty of the optimization problems and can achieve acceptable solutions using

low computing cost. Thus the mate-heuristic algorithms, like Particle Swarm Optimi-

zation (PSO) [3], Differential Evolution (DE) [2], Bat Algorithm (BA) [1], have at-

tracted great research interest for dealing with optimization applications.

New algorithms are still emerging, including krill herb algorithm [4], and social

spider optimization [5] et al. All these algorithms extract the swarm intelligence from

the laws of biological systems in nature. However, to learn from the nature for devel-

oping a better algorithm is still in progress.

In this paper, a new bio-inspired optimization algorithm, namely Chicken Swarm

Optimization (CSO) is proposed. It mimics the hierarchal order in the chicken swarm

and the behaviors of the chicken swarm. The chicken swarm can be divided into

several groups, each of which consists of one rooster and many hens and chicks.

Different chickens follow different laws of motions. There exist competitions between

different chickens under specific hierarchal order.

A New Bio-inspired Algorithm: Chicken Swarm Optimization 87

The rest of paper is organized as follows. Section 2 introduces the general biology

of the chicken. The details about the CSO are discussed in Section 3. The simulations

and comparative studies are presented in section 4. Section 5 summaries this paper

with some conclusions and discussions.

2 General Biology

As one of the most widespread domestic animals, the chickens themselves and their

eggs are primarily kept as a source of food. Domestic chickens are gregarious birds

and live together in flocks. They are cognitively sophisticated and can recognize over

100 individuals even after several months of separation. There are over 30 distinct

sounds for their communication, which range from clucks, cackles, chirps and cries,

including a lot of information related to nesting, food discovery, mating and danger.

Besides learning through trial and error, the chickens would also learn from their

previous experience and others’ for making decisions [6].

A hierarchal order plays a significant role in the social lives of chickens. The pre-

ponderant chickens in a flock will dominate the weak. There exist the more dominant

hens that remain near to the head roosters as well as the more submissive hens and

roosters who stand at the periphery of the group. Removing or adding chickens from

an existing group would causes a temporary disruption to the social order until a spe-

cific hierarchal order is established [7].

The dominant individuals have priority for food access, while the roosters may call

their group-mates to eat first when they find food. The gracious behavior also exists in

the hens when they raise their children. However, this is not the case existing for

individuals from different groups. Roosters would emit a loud call when other chick-

ens from a different group invade their territory [8].

In general, the chicken’s behaviors vary with gender. The head rooster would posi-

tively search for food, and fight with chickens who invade the territory the group

inhabits. The dominant chickens would be nearly consistent with the head roosters to

forage for food. The submissive ones, however, would reluctantly stand at the peri-

phery of the group to search for food. There exist competitions between different

chickens. As for the chicks, they search for the food around their mother.

Each chicken is too simple to cooperate with each other. Taken as a swarm,

however, they may coordinate themselves as a team to search for food under specific

hierarchal order. This swarm intelligence can be associated with the objective prob-

lem to be optimized, and inspired us to design a new algorithm.

3 Chicken Swarm Optimization

Given the aforementioned descriptions, we can develop CSO mathematically. For

simplicity, we idealized the chickens’ behaviors by the following rules.

(1) In the chicken swarm, there exist several groups. Each group comprises a do-

minant rooster, a couple of hens, and chicks.

88 X. Meng et al.

(2) How to divide the chicken swarm into several groups and determine the identi-

ty of the chickens (roosters, hens and chicks) all depend on the fitness values of the

chickens themselves. The chickens with best several fitness values would be acted as

roosters, each of which would be the head rooster in a group. The chickens with worst

several fitness values would be designated as chicks. The others would be the hens.

The hens randomly choose which group to live in. The mother-child relationship

between the hens and the chicks is also randomly established.

(3) The hierarchal order, dominance relationship and mother-child relationship in a

group will remain unchanged. These statuses only update every several (G) time steps.

(4) Chickens follow their group-mate rooster to search for food, while they may

prevent the ones from eating their own food. Assume chickens would randomly steal

the good food already found by others. The chicks search for food around their moth-

er (hen). The dominant individuals have advantage in competition for food.

Assume RN, HN, CN and MN indicate the number of the roosters, the hens, the

chicks and the mother hens, respectively. The best RN chickens would be assumed to

be roosters, while the worst CN ones would be regarded as chicks. The rest are treated

as hens. All N virtual chickens, depicted by their positions 

,



󰇛

󰇟

1,,

󰇠

,

󰇟

1, ,

󰇠

󰇜 at time step t, search for food in a D-dimensional space. In this work, the

optimization problems are the minimal ones. Thus the best RN chickens correspond to

the ones with RN minimal fitness values.

3.1 Movement of the Chickens

The roosters with better fitness values have priority for food access than the ones with

worse fitness values. For simplicity, this case can be simulated by the situation that

the roosters with better fitness values can search for food in a wider range of places

than that of the roosters with worse fitness values. This can be formulated below.



,





,



󰇛1

󰇛

0,



󰇜

󰇜 .

(1)







1 , 







exp 󰇛

󰇛









󰇜

|



|

󰇜, ,



󰇟

1,

󰇠

, .

(2)

Where Randn (0, 



) is a Gaussian distribution with mean 0 and standard devia-

tion 



. , which is used to avoid zero-division-error, is the smallest constant in the

computer. k, a rooster’s index, is randomly selected from the roosters group, f is the

fitness value of the corresponding x.

As for the hens, they can follow their group-mate roosters to search for food.

Moreover, they would also randomly steal the good food found by other chickens,

though they would be repressed by the other chickens. The more dominant hens

would have advantage in competing for food than the more submissive ones. These

phenomena can be formulated mathematically as follows.



,





,



1

,





,



2

,





,



 .

(3)

1exp

󰇛

󰇛







󰇜

/󰇛󰇛



󰇜󰇜󰇜 .

(4)

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