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Basic chicken swarm optimizer written in Python. Modified from theadaptive timestep PSO optimizer byjonathan46000 to keep a consistent format across optimizers in AntennaCAT.

Now featuring AntennaCAT hooks for GUI integration and user input handling.

• Chicken Swarm Optimization
• Requirements
• Implementation
  • Initialization
  • State Machine-based Structure
  • Importing and Exporting Optimizer State
  • Constraint Handling
  • Boundary Types
  • Multi-Objective Optimization
  • Objective Function Handling
    • Creating a Custom Objective Function
    • Internal Objective Function Example
  • Target vs. Threshold Configuration
• Examples
  • Basic Swarm Example
  • Detailed Messages
  • Realtime Graph
• References
• Related Publications and Repositories
• Licensing

The Chicken Swarm Optimization (CSO) algorithm, introduced by Meng et al. in 2014 [1], is inspired by the hierarchy and behaviors observed in a swarm of chickens, including roosters, hens, and chicks. Each type of bird has its own unique movement rules and interactions.

In CSO, there is an absence of a direct random velocity component, which is an important distinction from many PSO variations, though it is not the only variation without a velocity vector. Instead, the movements are based on specific behaviors and social interactions within the swarm. Generally, the movement rules for each type of bird in CSO can be described as:

1. Roosters:

   Roosters have the best positions (fitness values) in the swarm. They move based on their current position and the random perturbation to avoid getting stuck in local optima.

2. Hens:

   Hens follow roosters. They update their positions based on the positions of the roosters they follow and a randomly selected chicken. This reflects the social hierarchy and interaction in the swarm.

3. Chicks:

   Chicks follow their mother hens. They update their positions based on their mother's positions with some random factor to simulate the dependent behavior.

This project requires numpy, pandas, and matplotlib for the full demos. To run the optimizer without visualization, only numpy and pandas are requirements

Use 'pip install -r requirements.txt' to install the following dependencies:

contourpy==1.2.1cycler==0.12.1fonttools==4.51.0importlib_resources==6.4.0kiwisolver==1.4.5matplotlib==3.8.4numpy==1.26.4packaging==24.0pandas==2.2.3pillow==10.3.0pyparsing==3.1.2python-dateutil==2.9.0.post0pytz==2025.1six==1.16.0tzdata==2025.1zipp==3.18.1

Optionally, requirements can be installed manually with:

pipinstallmatplotlib,numpy,pandas

This is an example for if you've had a difficult time with the requirements.txt file. Sometimes libraries are packaged together.

# swarm variablesTOL=10**-6# Convergence ToleranceMAXIT=10000# Maximum allowed iterationsBOUNDARY=1# int boundary 1 = random,      2 = reflecting#              3 = absorbing,   4 = invisible# Objective function dependent variablesLB=func_configs.LB# Lower boundaries, [[0.21, 0, 0.1]]UB=func_configs.UB# Upper boundaries, [[1, 1, 0.5]]IN_VARS=func_configs.IN_VARS# Number of input variables (x-values)OUT_VARS=func_configs.OUT_VARS# Number of output variables (y-values)TARGETS=func_configs.TARGETS# Target values for output# Objective function dependent variablesfunc_F=func_configs.OBJECTIVE_FUNC# objective functionconstr_F=func_configs.CONSTR_FUNC# constraint function# chicken swarm specificRN=10# Total number of roostersHN=20# Total number of hensMN=15# Number of mother hens in total hensCN=20# Total number of chicksG=70# Reorganize groups every G steps# swarm setupbest_eval=1parent=None# for the PSO_TEST ONLYsuppress_output=True# Suppress the console output of particle swarmallow_update=True# Allow objective call to update state# Constant variablesopt_params= {'BOUNDARY': [BOUNDARY],# int boundary 1 = random,      2 = reflecting#              3 = absorbing,   4 = invisible'RN': [RN],# Total number of roosters'HN': [HN],# Total number of hens'MN': [MN],# Number of mother hens in total hens'CN': [CN],# Total number of chicks'G': [G]}# Reorganize groups every G stepsopt_df=pd.DataFrame(opt_params)mySwarm=swarm(LB,UB,TARGETS,TOL,MAXIT,func_F,constr_F,opt_df,parent=parent,evaluate_threshold=False,obj_threshold=None,decimal_limit=4)# arguments should take the form:# swarm([[float, float, ...]], [[float, float, ...]], [[float, ...]], float, int,# func, func,# dataFrame,# class obj,# bool, [int, int, ...],# int)## opt_df contains class-specific tuning parameters# boundary: int. 1 = random, 2 = reflecting, 3 = absorbing,   4 = invisible# RN: int# HN: int# MN: int# CN: int# G: int#

This optimizer uses a state machine structure to control the movement of the particles, call to the objective function, and the evaluation of current positions. The state machine implementation preserves the initial algorithm while making it possible to integrate other programs, classes, or functions as the objective function.

A controller with awhile loop to check the completion status of the optimizer drives the process. Completion status is determined by at least 1) a set MAX number of iterations, and 2) the convergence to a given target using the L2 norm. Iterations are counted by calls to the objective function.

Within thiswhile loop are three function calls to control the optimizer class:

• complete: thecomplete function checks the status of the optimizer and if it has met the convergence or stop conditions.
• step: thestep function takes a boolean variable (suppress_output) as an input to control detailed printout on current particle (or agent) status. This function moves the optimizer one step forward.
• call_objective: thecall_objective function takes a boolean variable (allow_update) to control if the objective function is able to be called. In most implementations, this value will always be true. However, there may be cases where the controller or a program running the state machine needs to assert control over this function without stopping the loop.

Additionally,get_convergence_data can be used to preview the current status of the optimizer, including the current best evaluation and the iterations.

The code below is an example of this process:

whilenotmyOptimizer.complete():# step through optimizer processing# this will update particle or agent locationsmyOptimizer.step(suppress_output)# call the objective function, control# when it is allowed to update and return# control to optimizermyOptimizer.call_objective(allow_update)# check the current progress of the optimizer# iter: the number of objective function calls# eval: current 'best' evaluation of the optimizeriter,eval=myOptimizer.get_convergence_data()if (eval<best_eval)and (eval!=0):best_eval=eval# optional. if the optimizer is not printing out detailed# reports, preview by checking the iteration and best evaluationifsuppress_output:ifiter%100==0:#print out every 100th iteration updateprint("Iteration")print(iter)print("Best Eval")print(best_eval)

Some optimizer information can be exported or imported. This varies based on each optimizer.

Optimizer state can be exported at any step. When importing an optimizer state, the optimizer should be initialized first, and then the state information can be imported via a Python pickle file. Other methods can be used if custom code is written to handle preprocessing.

Returning data from optimizer and saving to a .pkl file:

data=demo_optimizer.export_swarm()data_df=pd.DataFrame(data)print(data_df)data_df.to_pickle('output_data_df.pkl')

Importing data from a .pkl file and importing it into the optimizer:

data_df=pd.read_pickle('output_data_df.pkl')demo_optimizer.import_swarm(data_df)

Users must create their own constraint function for their problems, if there are constraints beyond the problem bounds. This is then passed into the constructor. If the default constraint function is used, it always returns true (which means there are no constraints).

This optimizers has 4 different types of bounds, Random (Particles that leave the area respawn), Reflection (Particles that hit the bounds reflect), Absorb (Particles that hit the bounds lose velocity in that direction), Invisible (Out of bound particles are no longer evaluated).

Some updates have not incorporated appropriate handling for all boundary conditions. This bug is known and is being worked on. The most consistent boundary type at the moment is Random. If constraints are violated, but bounds are not, currently random bound rules are used to deal with this problem.

The no preference method of multi-objective optimization, but a Pareto Front is not calculated. Instead the best choice (smallest norm of output vectors) is listed as the output.

The objective function is handled in two parts.

• First, a defined function, such as one passed in fromfunc_F.py (see examples), is evaluated based on current particle locations. This allows for the optimizers to be utilized in the context of 1. benchmark functions from the objective function library, 2. user defined functions, 3. replacing explicitly defined functions with outside calls to programs such as simulations or other scripts that return a matrix of evaluated outputs.

• Secondly, the actual objective function is evaluated. In the AntennaCAT set of optimizers, the objective function evaluation is either aTARGET orTHRESHOLD evaluation. For aTARGET evaluation, which is the default behavior, the optimizer minimizes the absolute value of the difference of the target outputs and the evaluated outputs. ATHRESHOLD evaluation includes boolean logic to determine if a 'greater than or equal to' or 'less than or equal to' or 'equal to' relation between the target outputs (or thresholds) and the evaluated outputs exist.

Future versions may include options for function minimization when target values are absent.

Custom objective functions can be used by creating a directory with the following files:

• configs_F.py
• constr_F.py
• func_F.py

configs_F.py contains lower bounds, upper bounds, the number of input variables, the number of output variables, the target values, and a global minimum if known. This file is used primarily for unit testing and evaluation of accuracy. If these values are not known, or are dynamic, then they can be included experimentally in the controller that runs the optimizer's state machine.

constr_F.py contains a function calledconstr_F that takes in an array,X, of particle positions to determine if the particle or agent is in a valid or invalid location.

func_F.py contains the objective function,func_F, which takes two inputs. The first input,X, is the array of particle or agent positions. The second input,NO_OF_OUTS, is the integer number of output variables, which is used to set the array size. In included objective functions, the default value is hardcoded to work with the specific objective function.

Below are examples of the format for these files.

configs_F.py:

OBJECTIVE_FUNC=func_FCONSTR_FUNC=constr_FOBJECTIVE_FUNC_NAME="one_dim_x_test.func_F"#format: FUNCTION NAME.FUNCTIONCONSTR_FUNC_NAME="one_dim_x_test.constr_F"#format: FUNCTION NAME.FUNCTION# problem dependent variablesLB= [[0]]# Lower boundariesUB= [[1]]# Upper boundariesIN_VARS=1# Number of input variables (x-values)OUT_VARS=1# Number of output variables (y-values)TARGETS= [0]# Target values for outputGLOBAL_MIN= []# Global minima sample, if they exist.

constr_F.py, with no constraints:

defconstr_F(x):F=TruereturnF

constr_F.py, with constraints:

defconstr_F(X):F=True# objective function/problem constraintsif (X[2]>X[0]/2)or (X[2]<0.1):F=FalsereturnF

func_F.py:

importnumpyasnpimporttimedeffunc_F(X,NO_OF_OUTS=1):F=np.zeros((NO_OF_OUTS))noErrors=Truetry:x=X[0]F=np.sin(5*x**3)+np.cos(5*x)* (1-np.tanh(x**2))exceptExceptionase:print(e)noErrors=Falsereturn [F],noErrors

There are three functions included in the repository:

1. Himmelblau's function, which takes 2 inputs and has 1 output
2. A multi-objective function with 3 inputs and 2 outputs (see lundquist_3_var)
3. A single-objective function with 1 input and 1 output (see one_dim_x_test)

Each function has four files in a directory:

1. configs_F.py - contains imports for the objective function and constraints, CONSTANT assignments for functions and labeling, boundary ranges, the number of input variables, the number of output values, and the target values for the output
2. constr_F.py - contains a function with the problem constraints, both for the function and for error handling in the case of under/overflow.
3. func_F.py - contains a function with the objective function.
4. graph.py - contains a script to graph the function for visualization.

Other multi-objective functions can be applied to this project by following the same format (and several have been collected into a compatible library, and will be released in a separate repo)

Plotted Himmelblau’s Function with 3D Plot on the Left, and a 2D Contour on the Right

Plotted Multi-Objective Function Feasible Decision Space and Objective Space with Pareto Front

Plotted Single Input, Single-objective Function Feasible Decision Space and Objective Space with Pareto Front

Local minima at$(0.444453, -0.0630916)$

Global minima at$(0.974857, -0.954872)$

An April 2025 feature is the user ability to toggle TARGET and THRESHOLD evaluation for the optimized values. The key variables for this are:

# Boolean. use target or threshold. True = THRESHOLD, False = EXACT TARGETevaluate_threshold=True# arrayTARGETS=func_configs.TARGETS# Target values for output from function configs# OR:TARGETS= [0,0,0]#manually set BASED ON PROBLEM DIMENSIONS# threshold is same dims as TARGETS# 0 = use target value as actual target. value should EQUAL target# 1 = use as threshold. value should be LESS THAN OR EQUAL to target# 2 = use as threshold. value should be GREATER THAN OR EQUAL to target#DEFAULT THRESHOLDTHRESHOLD=np.zeros_like(TARGETS)# ORTHRESHOLD= [0,1,2]# can be any mix of TARGET and THRESHOLD

To implement this, the originalself.Flist objective function calculation has been replaced with the functionobjective_function_evaluation, which returns a numpy array.

The original calculation:

self.Flist=abs(self.targets-self.Fvals)

Whereself.Fvals is a re-arranged and error checked returned value from the passed in function fromfunc_F.py (see examples for the internal objective function or creating a custom objective function).

When using a THRESHOLD, theFlist value corresponding to the target is set to epsilon (the smallest system value) if the evaluatedfunc_F value meets the threshold condition for that target item. If the threshold is not met, the absolute value of the difference of the target output and the evaluated output is used. With a THRESHOLD configuration, each value in the numpy array is evaluated individually, so some values can be 'greater than or equal to' the target while others are 'equal' or 'less than or equal to' the target.

main_test.py provides a sample use case of the optimizer.

main_test_details.py provides an example using a parent class, and the self.suppress_output flag to control error messages that are passed back to the parent class to be printed with a timestamp. This implementation sets up the hooks for integration with AntennaCAT in order to provide the user feedback of warnings and errors.

main_test_graph.py provides an example using a parent class, and the self.suppress_output flag to control error messages that are passed back to the parent class to be printed with a timestamp. Additionally, a realtime graph shows particle locations at every step.

NOTE: if you close the graph as the code is running, the code will continue to run, but the graph will not re-open.

[1] X. B. Meng, Y. Liu, X. Gao, and H. Zhang, "A new bio-inspired algorithm: Chicken swarm optimization," in Proc. Int. Conf. Swarm Intell. Cham, Switzerland, Springer, 2014, pp. 86–94.

This software works as a stand-alone implementation, and as one of the optimizers integrated into AntennaCAT.

The code in this repository has been released under GPL-2.0