Lotka Volterra multi-arcs problem

From mintOC
Revision as of 15:00, 21 December 2017 by ClemensZeile (Talk | contribs) (Created page with "{{Dimensions |nd = 1 |nx = 3 |nw = 1 |nre = 3 }}<!-- Do not insert line break here or Dimensions Box moves up in the layout... -->This site describ...")

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
Lotka Volterra multi-arcs problem
State dimension: 1
Differential states: 3
Discrete control functions: 1
Interior point equalities: 3

This site describes a Lotka Volterra variant with three singular arcs instead of only one in the standard variant.

Mathematical formulation

The mixed-integer optimal control problem is given by


\begin{array}{llclr}
 \displaystyle \min_{x, w} & x_2(t_f)   \\[1.5ex]
 \mbox{s.t.} 
 & \dot{x}_0 & = &  x_0 - x_0 x_1 - \;  c_0\; x_0 \; w, \\
 & \dot{x}_1 & = & - x_1 + x_0 x_1 - \; c_1\; x_1 \; w,  \\
 & \dot{x}_2 & = & (x_0 - p_0(t))^2 + (x_1 - p_1(t))^2,  \\[1.5ex]
 & x(0) &=& (0.5, 0.7, 0)^T, \\
 & w(t) &\in&  \{0, 1\}, 
\end{array}

Here the differential states (x_0, x_1) describe the biomasses of prey and predator, respectively. The third differential state is used here to transform the objective, an integrated deviation, into the Mayer formulation \min \; x_2(t_f). This problem variant allows to choose between three different fishing options.

Parameters

These fixed values are used within the model.

Failed to parse (PNG conversion failed; check for correct installation of latex and dvipng (or dvips + gs + convert)): {\begin{array}{rcl}[t_{0},t_{f}]&=&[0,18],\\(c_{0},c_{1},)&=&(0.4,0.2),\\p_{{0}}(t)&=&\left\{{\begin{array}[ll]1&{\text{for }}t\in [0,7.2]\end{array}}\right.\\p_{1}(t)&=&p_{0}(t).\end{array}}


Reference Solutions

If the problem is relaxed, i.e., we demand that w(t) be in the continuous interval [0, 1] instead of the binary choice \{0,1\}, the optimal solution can be determined by means of direct optimal control.

The optimal objective value of the relaxed problem with  n_t=12000, \, n_u=400  is x_2(t_f) =1.82875272. The objective value of the binary controls obtained by Combinatorial Integral Approimation (CIA) is x_2(t_f) =1.82878681.