In the field of optimization, many algorithms were being proposed recent years, e.g. Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) etc. Some of these optimization algorithms were developed based on swarm intelligence.

Cat swarm optimization (CSO) is a swarm Intelligence algorithm, which was originally invented by Chu et al. in 2006. It is inspired by the natural behavior of cats, and it has a novel technique in modeling exploration and exploitation phases. It has been successfully applied in various optimization fields of science and engineering. However, the literature lacks a recent and detailed review of this algorithm. In addition, since 2006, CSO has not been compared against novel algorithms, i.e., it has been mostly compared with PSO algorithm while many new algorithms have been introduced since then.

Behaviors of Cats

According to the classification of biology, there are about thirty-two different species of Creatures in feline, e.g. Lion, tiger, leopard, cat etc. Though they have different living Environments, there are still many behaviors simultaneously exist in most of felines. Despite the hunting skill is not innate for felines, it can be trained to acquire. For The wild felines, the hunting skill ensures the survival of their races, but for the indoor Cats, it exhibits the instinct of strongly curious about any moving things. Though all cats have the strong curiosity, they are, in most times, inactive. If you Spend some time to observe the existence of cats, you may easily find that the cats. The alertness of cats is very high, they always stay alert even if they are resting.

Proposed Algorithm

In our proposed Cat Swarm Optimization, we first model the major two behaviors of Cats into two sub-models, namely, seeking mode and tracking mode. By the way of mingling with these two modes with a user-defined proportion, CSO can present better performance.

Seeking mode

There are certain terms required in this mode.

(a) The number of copies of a cat produced in seeking mode is called Seeking Memory Pool (SMP). (b) The maximum difference between the new and old values in the dimension selected for     mutation is called Seeking Range of selected Dimension (SRD).

(c) The number of dimensions to be mutated is called Counts of Dimension to Change (CDC).

(d) To guarantee that the cats spend most of their time resting and observing, i.e., most of the time     is spent in seeking mode, a term called mixture ratio (MR), which is a fraction of population  allocated a very small value.

 The steps executed in Seeking mode are:

  1. Select randomly MR fraction of population np as Seeking cats; the rest are Tracing cats. Create SMP copies of it Seeking cat.
  2. Based on CDC update the position of each copy by randomly adding or subtracting SRD fraction of the present position value.
  3. Evaluate the error fitness values of all copies.
  4. Pick the best candidate from all copies and place it at the position of it Seeking cat.
  5. Repeat Step 2 till all Seeking cats are involved.



A novel Cat Swarm Optimization (CSO) algorithm is applied for the solution of the constrained, multi-modal optimal FIR filter design problems. Comparison of the results of PM, RGA, PSO, DE and the CSO algorithms has been made. It is revealed that the CSO can converge very fast to the best quality optimal solution and possesses the best convergence characteristics with the least execution times.

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International journal of swarm intelligence and evolutionary computation