Rank: 75 based on 720 downloads (last 30 days) and 12 files submitted
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Seyedali Mirjalili

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Multi-objective optimization, Robust optimization, Swarm intelligence, Computational intelligence

 

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Files Posted by Seyedali View all
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(last 30 days)
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02 Sep 2014 Screenshot Adaptive gbest-guided gravitational search algorithm A gbest-guided gravitational search algorithm Author: Seyedali Mirjalili optimization, gsa, heuristic, evolutionary algorith..., ggsa, adaptive gbestguided ... 75 0
31 Jul 2014 Screenshot Binary Bat Algorithm BBA is the binary version of the Bat Algorithm for optimizing binary problems. Author: Seyedali Mirjalili binary optimization, optimization, binary search space, ba, bba, swarm intelligence 92 1
  • 5.0
5.0 | 1 rating
29 Jul 2014 Screenshot Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili optimization, metaheuristics, heuristic, optimization algorith..., grey wolf optimizer, gwo 96 9
  • 5.0
5.0 | 2 ratings
24 Jul 2014 Screenshot Enhanced Binary Particle Swarm Optimization (BPSO) with 6 new transfer functions An Enhanced binary Particle Swarm Optimization algorithm (VPSO) with v-shaped transfer functions Author: Seyedali Mirjalili particle swarm optimi..., pso, binary particle swarm..., discrete particle swa..., bpso, discrete optimization 58 0
22 Jul 2014 Screenshot Chaotic Biogeography-based Optimisation (CBBO) algorithm 50 chaos-embedded versions of the BBO algorithm Author: Seyedali Mirjalili optimization, bbo, optimisation, algorithm, chaos theory, chaotic maps 42 0
Comments and Ratings by Seyedali View all
Updated File Comments Rating
30 Aug 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili

Hi Amr Hessan,

My thanks for identifying this issue. You are right. Every time alpha updates, we have to update beta and delta and well. I will consider this and update the files soon.

01 May 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili

Hi Dalia,

This problem originates from your F5 function.

o=(100*(x(2:dim)-(x(1:dim-1).^2)).^2+(1-x(1:dim-1)).^2) returns a vector, while there should be a single fitness value for each search agent.
The formula for the F5 function has a sum in front of the expression as follows:
o=sum(100*(x(2:dim)-(x(1:dim-1).^2)).^2+(x(1:dim-1)-1).^2);

Generally speaking, each search agent should be assigned with only one fitness value.

Change your F5 function so that it returns a single value for each input vector, give it a try, and let me know.

Ali

08 Feb 2014 Improved Feedforward Neural Networks Using PSOGSA This program is an improved Feedforward Neural Network using a hybrid algorithm called PSOGSA. Author: Seyedali Mirjalili

Hi Sonmyadeep,

It depends on your dataset. You need to change FNNPSOGSA and My_FNN files according to your dataset. The codes are well-documented, so I believe if you spend time and understand the coeds it would be easy for you to modify it.

07 Feb 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili

Daniil,

You are right, some search agents may go beyond the boundaries of the search space in the last iteration and there is no more chance for them to be returned back to the search space. I moved position boundary checking to the end of the second for loop (right after updating the positions). I have also updated the source file accordingly.

Thanks for that and please let me know if there was any other issue.

06 Feb 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili

Hi Daniil,

Thanks for your compliment and comment. I think the current codes work correctly because it is important to return back the search agents that go beyond the boundaries when you want to calculate their "fitnesses". I agree with you that we can do this at the end of the second for loop where the positions are updated or at the top of the firs for loop. However, the important issue here is that the boundary checking should be done right before updating the fitnesses. I hope these make sense. Once again, thanks for your vigilance and pointing out this matter.

Regards,
Ali

Comments and Ratings on Seyedali's Files View all
Updated File Comment by Comments Rating
30 Aug 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili Mirjalili, Seyedali

Hi Amr Hessan,

My thanks for identifying this issue. You are right. Every time alpha updates, we have to update beta and delta and well. I will consider this and update the files soon.

26 Aug 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili Hassan, Amr

Hi, Thanks for your code. good job
just have one concern. and please correct me if I was wrong. when updating Alpha, Beta and Delta Positions and score
[quote]
fitness=fobj(Positions(i,:)); %Amr: best_tour = calculate tour length in tsp
% Update Alpha, Beta, and Delta
if fitness<Alpha_score
Alpha_score=fitness; % Update alpha
Alpha_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness<Beta_score
Beta_score=fitness; % Update beta
Beta_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score
Delta_score=fitness; % Update delta
Delta_pos=Positions(i,:);
end
[/quote]
this doesn't correctly update Beta and Delta. consider this scenario:
for example, after 3rd iteration alpha_score=1000, Beta_score=1200, Delta_score=1500
if the fitness of 4th iteration is 800 then alpha_score will be updated and beta_score will remain 1200 while it should be 1000 and the same for delta_score
proposed correction,

[quote]
fitness=fobj(Positions(i,:)); %Amr: best_tour = calculate tour length in tsp
% Update Alpha, Beta, and Delta
if fitness<Alpha_score
Beta_score=Alpha_score;
Beta_pos=Alpha_pos;
Alpha_score=fitness; % Update alpha
Alpha_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness<Beta_score
Delta_score=Beta_score;
Delta_pos=Beta_pos;
Beta_score=fitness; % Update beta
Beta_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score
Delta_score=fitness; % Update delta
Delta_pos=Positions(i,:);
end
[/quote]

09 Aug 2014 Improved Feedforward Neural Networks Using PSOGSA This program is an improved Feedforward Neural Network using a hybrid algorithm called PSOGSA. Author: Seyedali Mirjalili Sovann, Narin

Dear Seyedali Mirjalili,
i'd modified your codes, FNNPSO, to deal with load forecasting problem(to train FNN). result is good for less dimensional search space (2 dimensions) but not good for more dimensional search space (9 dimensions).
Any advises or suggestions, pleas help
All best regards,
Narin

22 Jul 2014 Grey Wolf Optimizer (GWO) GWO is a novel meta-heuristic algorithm for global optimization Author: Seyedali Mirjalili iman

22 Jul 2014 Grey Wolf Optimizer Toolbox A toolbox for the Grey Wolf Optimizer (GWO) algorithm Author: Seyedali Mirjalili iman

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