Hybrid Particle Swarm and Grey Wolf Optimization

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This repository has the code for hybrid particle swarm optimization (PSO) and grey wolf optimization (GWO). Code is tested on benchmark functions and compared with GWO only. It has been found that hybrid PSOGWO performs better than PSO. The description is provided along with it.

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Description

The hybrid Particle Swarm Optimization and Grey Wolf Optimization algorithm is low level because we merge the functionalities of both of them. Both the algorithms  run in parallel.

  1. firstly make the statement of the PSOGWO function
[Best_score,Best_pos,GWO_cg_curve]=PSOGWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj) 

In above statement the input parameter is mainly a benchmark function which is represented by a ‘fobj’ and others are lb=lower bound limit and ub=upper bound limit. There are three agents position is initialize Alpha position , Beta position and Delta position. Velocity and weight parameters are calculated by the formula given below

Velocity = .3*randn (SearchAgents_no,dim)                        
w=0.5+rand()/2 
  1. Initialize position of search agents by calling the function
Positions=initialization(SearchAgents_no,dim,ub,lb);  

In this statement the upper bound and lower bound limits are available. The search agents position is randomly search. The each search agents have different upper and lower bound limits. Calculate the initial position of the search variable. We initialize the parameters of algorithm, generate and also evaluate the initial position, and then determine the best solution in the position.

  1. Call the benchmark function

Benchmark function is represented by the ‘fobj’and find the initial best fitness value for benchmark objective function. The fobj function contains all the information about the benchmark function. It has 23 different benchmark function cases which have different dimension, upper bound and lower bound limits. We can randomly take any benchmark (F1 …..F23) objective function.

  1. Start the main while loop (t< max no. of iteration)

Then start the main loop for the maximum iterations. Then update the position of the search agents. After updating the position  the upper and lower bound limits are applied and update the position of search agents by using equation

Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb       
  1. Evaluate the fitness position of search agents by using the equation
fitness=fobj(Positions(i,:))   

The fitness value is obtained by using equation 4. Then update the three position which is describe by Alpha, Beta and Delta position

 If,   fitness<Alpha score && Alpha score=fitness

Then, Alpha Position=Positions (i,:)

If , fitness>Alpha score && fitness<Beta score && Beta score=fitness

Beta position=Positions (i,:)

If,  fitness>Alpha score && fitness>Beta score && fitness<Delta score

  Delta score=fitness

Delta position=Positions (i,:)

These three positions are the new position of the Wolfs. We obtained three best fit position but now update these three position randomly.

  1. Update the positions of first three agents

Using equation number 7, 8 and 9 update the values and these values represented by X1, X2 and X3.

X1=Alpha position (j)-A1*D_alpha                          
X2=Beta position (j)-A2*D_beta                               
X3=Delta position (j)-A3*D_delta 
  1. Update the velocity and position now as shown in equation 13 and 14 by using the

   

Or in case of matlab code the equations are written as

 velocity(i,j)=w*(velocity(i,j)+C1*r1*(X1-Positions(i,j))+C2*r2*(X2-Positions(i,j))+C3*r3*(X3-Positions(i,j)))
  Positions(i,j)=Positions(i,j)+velocity(i,j)
  1. Write another main script which perform the action on benchmark function
  • Initialize the search agents, iterations and bench mark function
  • Call the bench mark function detailed
  • Call the PSOGWO function which initialize and figure out the fitness value for any particular function
  • Call the function plot for graphs and convergence curve for the benchmark function. The best position and best fitness value obtained by using the hybrid PSOGWO algorithm is shown by the curve.

After update the position of the search agents and particle velocity the fitness value save in the Alpha score. Then plot a convergence curve according to the search space for any benchmark function. We obtained results which are better than the GWO. So the PSOGWO hybrid approach is good for low level algorithms.

Published Paper similar to this work

  1. Shaheen, M. A., Hasanien, H. M., & Alkuhayli, A. (2021). A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution. Ain Shams Engineering Journal, 12(1), 621-630.
  2. Şenel, F. A., Gökçe, F., Yüksel, A. S., & Yiğit, T. (2019). A novel hybrid PSO–GWO algorithm for optimization problems. Engineering with Computers, 35(4), 1359-1373.
  3. Kamboj, V. K. (2016). A novel hybrid PSO–GWO approach for unit commitment problem. Neural Computing and Applications, 27(6), 1643-1655.
  4. Abdelshafy, A. M., Hassan, H., & Jurasz, J. (2018). Optimal design of a grid-connected desalination plant powered by renewable energy resources using a hybrid PSO–GWO approach. Energy conversion and management, 173, 331-347.
  5. Chopra, N., Kumar, G., & Mehta, S. (2016). Hybrid GWO-PSO algorithm for solving convex economic load dispatch problem. Int J Res Adv Technol, 4(6), 37-41.

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