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MSSPSO.m
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MSSPSO.m
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function [evals_before, swarm_before, X_before, Y_before, evals_after, ...
swarm_after, X_after, Y_after] = ...
MSSPSO(swarm_size, swarm_num, max_evaluations, problem_func,...
problem_func_params, param_num, mn, mx)
% Implementation of the algorithm described in:
% "Multi-Sub-Swarm Particle Swarm Optimization Algorithm for Multimodal
% Function Optimization"
% by Jun Zhang et al.
% published in Proceedings of the IEEE Congress on Evolutionary Computation,
% pages 3215-3220, 2007
%
% Implementation provided corresponds to that used in:
%"Running Up Those Hills: Multi-Modal Search with the Niching Migratory
% Multi-Swarm Optimiser"
% by Jonathan E. Fieldsend
% published in Proceedings of the IEEE Congress on Evolutionary Computation,
% pages 2593-2600, 2014
%
% Please reference both papers if you undertake work utilising this code.
%
% Implementation (c) by Jonathan Fieldsend, University of Exeter, 2014
%
% Assumes function maximisation
%
% REQUIRED ARGUMENTS
%
% pop_size = population size
% max_evaluations = maximum number of evaluations to be taken through the
% problem function
% problem_func = string containing function to be optimised
% problem_func_params = meta-parameters needed by test function (distinct
% from optimisation (design) parameters
% param_num = number of design parameters
% mn = minimum design parameter values (a vector with param_num elements)
% mx = maximum design parameter values (a vector with param_num elements)
%
% OUTPUTS
%
% Due to the algorithms design, repeated generations will often not lead to
% exactly 'evals' function evaluations. As such it returns the state of the
% algorithm in the generation before the evals limit is breached, and
% the state after it is breached
%
% evals_before = number of function evaluations used but generation
% directly prior to limit breach
% swarm_before = structure containing state of swarm(s) at 'evals_before' function
% evaluations
% X_before = matrix containing location of all the swarms pbests (first half)
% and current particle locations (second half) at evals_before
% Y_before = function response at X locations at evals_before
%
% evals_after = number of function evaluations used but generation
% directly prior to limit breach
% swarm_after = structure containing state of swarm(s) at 'evals_after' function
% evaluations
% X_after = matrix containing location of all the swarms pbests (first half)
% and current particle locations (second half) at evals_after
% Y_after = function response at X locations at evals_after
if (max_evaluations<=0)
error('max_evaluations argument must be positive');
end
% at start no evaluations used, and swarm structure is empty
evals_after = 0;
swarm_after = [];
X_after=[];
Y_after=[];
while (evals_after < max_evaluations)
evals_before = evals_after;
swarm_before = swarm_after;
X_before = X_after;
Y_before = Y_after;
[evals_after, swarm_after, X_after, Y_after] = ...
MSSPSO_iterative(swarm_size, swarm_num, max_evaluations, problem_func,...
problem_func_params, param_num, mn, mx, evals_after, swarm_after);
end