Intelligent optimization algorithm: cheetah optimization algorithm - with code

Intelligent optimization algorithm: Cheetah optimization algorithm

Abstract: The CO algorithm is a new swarm intelligence optimization algorithm proposed by Mohammad AminAkbari et al. in 2022 inspired by cheetah hunting in nature. The algorithm realizes location update by simulating the three strategies of cheetahs searching, waiting and attacking during hunting. It has the characteristics of strong optimization ability and fast convergence speed.

1. Cheetah optimization algorithm

1.1 Initialization

(1) Initialization. Similar to other swarm intelligence optimization algorithms, the CO algorithm also starts from population initialization. set in $In\; the\; d$ -dimensional search space, the initial position of the cheetah is described as:
$$X_{i,j}=L{B}_j+\mathrm{rand}\left(U{B}_j-L{B}_j\right)i=1,2,\cdots ,n;j=1,2,\cdots ,d\text{\hspace{1em}( 1 )}$$
where:$X_{i,j}$for $i$ head cheetah$j$维设计; $U{B}_j、L{B}_j$for the $The\; upper\; and\; lower\; limits\; of\; the\; j$ -dimensional search space; rand is a random number between 0 and 1;$n$ is the cheetah population size;$d$ is the problem dimension.

1.2 Search strategy

Cheetahs conduct full range scans or active searches in their territory (search space) or surrounding area to find prey. The mathematical description of this strategy is:
$$X_{i,j}^{t+1}=X_{i,j}^t+{\bar{r}}_{i,j}^{-1}\cdot a_{i,j}^{t}t=1,2,\cdots ,T\text{\hspace{1em}( 2 )}$$
where:$X_{i,j}^{t+1}$for $i$ cheetahs$t+1$ iteration$j$维设计; $X_{i,j}^t$for $i$ head cheetah$t$ iteration$j$ -dimensional position;${\bar{r}}_{i,j}$for $i$ head cheetah$j$ -dimensional random number with normal distribution;$a_{i,j}^t$for $i$ head cheetah$t$ iterationjjThe search step in dimension$j$$T$ is the maximum number of iterations of the algorithm.

1.3 Sit and wait strategy

(3) Sit and wait strategy. In search mode, the prey may be exposed to the cheetah's vision, in which case every movement of the cheetah may cause the prey to escape. To avoid this, cheetahs adopt a sit-and-wait ambush strategy (lying on the ground or hiding in bushes) to get close to their prey. The mathematical description of the strategy is:
$$X_{i,j}^{l+1}=X_{i,j}^t\text{\hspace{1em}( 3 )}$$
The meanings of the parameters in formula (3) are the same as above. This strategy not only improves the hunting success rate (obtaining the optimal solution), but also avoids premature harvesting of CO.

1.4 Attack strategy

In the CO algorithm, each cheetah can adjust its own position according to the position of the escaping prey, the leading cheetah or the nearby cheetahs to obtain the best attacking position. The mathematical description of this strategy is:
$$X_{i,j}^{t+1}=X_{B,j}^t+{\bar{r}}_{i,j}\cdot b_{i,j}^t\text{\hspace{1em}( 4 )}$$
where:$X_{B,j}^t$for the $t$ iteration$The\; j-$ dimensional prey position, namely the current best position;${\bar{r}}_{i,j}$Because the $i$ head cheetah$j-$ dimensional steering factor;$b_{i,j}^t$for $i$ head cheetah$t$ iteration$The\; j-$ dimensional interaction factor reflects the interaction between cheetahs or between cheetahs and the leading cheetah; the meanings of other parameters are the same as above.

2. Experimental results

3. References

[1] AKBARI M A, ZARE M, AZIZIPANAH-ABARGHOOEE R, et al.The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems[J]. Scientific reports, 2022, 12(1): 1-20.

[2] Li Jie, Cui Dongwen. Several new swarm intelligence algorithms optimize Gaussian process regression to predict annual precipitation [J]. Water Saving Irrigation, 2023 (07): 96-103+109.

4.Matlab

5.python