Cat Swarm Optimization Algorithm is an Metaheuristic Optimization Algorithm. It comes in the category of Nature Inspired Swarm Based Optimization Algorithm. As we all know Nature Inspired Swarm Based Optimization Algorithms are Stochastic methods designed to solve different optimization problem's. Cat Swarm Optimization Algorithm is inspired by behavior of cats in real life. Cat Swarm Optimization Algorithm is invested in 2006 by Shu Chuan Chu. Author tested Cat Swarm Optimization Algorithm using 23 Classical benchmark functions and 10 modern benchmark function.
Two sub-models are used in Cat Swarm Optimization Algorithm to describe cat's behavior. They are modeled on Cats behavior and play major role in Cat Swarm Optimization Algorithm.
Seeking Mode.
Tracing Mode.
In Seeking Mode, Cat Swarm Optimization Algorithm aims to show cats resting, not moving [in the stationary state]. As we know, Cats observe environment before hunt. Seeking mode is used to describe the resting behavior of the cats [when cats are resting, observing environment, seeking the best position to move]. In seeking mode 4 Parameters are used to represent cat moves, stationary sates and next move. All these values for these parameter's are tuned and defined by users using Trial and Error method. 4 parameters used in seeking mode:
Seeking Memory Pool (SMP).
Seeking Range of Selected dimension (SRD).
Counts of dimension to change (CDC).
Self Position Considering (SPC).
Tracing Mode represent target tracing by cat after finding it.
Cat Swarm Optimization Algorithm Steps:
Step 01: Specify upper bound and lower bound for solution set.
Step 02: Randomly initialize population for N cats and spread them in the dimensional search space where each cat has random velocity values within pre-defined maximum velocity range.
Step 03: Randomly classify cats into Seeking mode and Tracing mode according to MR value.
Step 04: Calculate fitness values for each cat and save best cat position in the memory.
Step 05: Move cat's into either tracing mode to seeking mode.
Step 06: According to MR value redistribute cats into seeking mode or tracing mode.
Step 07: If stopping criteria is matched stop else repeat from Step 04 to Step 06.
PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2] 1.) Initialize Population [Current Iteration (t) = 0] Population Size = 4; 𝑥𝑖 : (i = 1,2,3,4) and (t = 0) 𝑥1 =1.3; 𝑥2=4.3; 𝑥3=0.4; 𝑥4=−1.2 2.) Fitness Function u...
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