Hajime-Yabumoto-Mori-Yoshikazu
Problem definition
Objective function
f = @(x) [x(:,1).^2 - x(:,2), -0.5*x(:,1) - x(:,2) - 1]
Optimization settings
o = struct% initializing struct
g1 = @(x) 6.5 - x(:,1)./6 - x(:,2) g2 = @(x) 7.5 - 0.5*x(:,1) - x(:,2) g3 = @(x) 30 - 5*x(:,1) - x(:,2) o.g = @(x) g1(x).*(g1(x)<0) + g2(x).*(g2(x)<0) + g3(x).*(g3(x)<0)% constraints
o.d = 2% dimension of decision variable
o.lb = -7% lower bounds
o.ub = 4% upper bounds
Problem properties
| dimension | objectives | smoothness |
| 2 | 2 | - |
Optimization example with nsga2
Algorithm options
o.maxit = 40% number of iterations
o.pop = 100% number of population
Optimization
rng(0)% for tractability
[popPos,popFront,popCost,popInfo,traceIt] = nsga2(f,o)% running minimization
Animation
rng(0) [~,~,~,~,~,info] = nsga2(f,o) info.plot = 'pareto' info.animate = true% plot animation
info.animfreq = 4% frame frequency
optimview('nsga2',info)
References
[1] Y. Hajime, N. Yabumoto, Mori and Yoshikazu, "Multiobjective optimization by means of the thermodynamical genetic algorithm", The 4th Int. Conf. on Parallel Problem Solving from Nature, pages 504-512, 1996