Merge pull request #165 from bluescarni/pr/to_release

Preparing 2.7

doc/sphinx/changelog.rst

@@ -1,7 +1,7 @@
 Changelog
 =========
 
-2.7 (unreleased)
+2.7 (2018-04-13)
 ----------------
 
 New

doc/sphinx/docs/cpp/algorithms/pso_gen.rst

@@ -0,0 +1,5 @@
+Particle Swarm Optimization Generational (GPSO)
+===========================================================
+
+.. doxygenclass:: pagmo::pso_gen
+   :members:

doc/sphinx/docs/cpp/cpp_docs.rst

@@ -37,6 +37,7 @@ Implemented algorithms
   algorithms/nlopt
   algorithms/nsga2
   algorithms/pso
+  algorithms/pso_gen
   algorithms/sade
   algorithms/sea
   algorithms/sga

doc/sphinx/docs/python/algorithms/py_algorithms.rst

@@ -21,6 +21,7 @@ Differential Evolution (DE)                                :class:`pygmo.de`
 Self-adaptive DE (jDE and iDE)                             :class:`pygmo.sade`                       S-U             Exposed from C++
 Self-adaptive DE (de_1220 aka pDE)                         :class:`pygmo.de1220`                     S-U             Exposed from C++
 Particle Swarm Optimization (PSO)                          :class:`pygmo.pso`                        S-U             Exposed from C++
+Particle Swarm Optimization Generational (GPSO)            :class:`pygmo.pso_gen`                    S-U             Exposed from C++
 (N+1)-ES Simple Evolutionary Algorithm                     :class:`pygmo.sea`                        S-U (sto)       Exposed from C++
 Simple Genetic Algorithm                                   :class:`pygmo.sga`                        S-U (sto)       Exposed from C++
 Corana's Simulated Annealing (SA)                          :class:`pygmo.simulated_annealing`        S-U             Exposed from C++
@@ -129,6 +130,11 @@ Algorithms exposed from C++
 
 -------------------------------------------------------------
 
+.. autoclass:: pygmo.pso_gen
+   :members:
+
+-------------------------------------------------------------
+
 .. autoclass:: pygmo.nsga2
    :members:
 

pygmo/docstrings.cpp

@@ -1150,7 +1150,8 @@ The best known solution for the )"
            + name + R"( problem.
 
 Returns:
-    1D NumPy float array: the best known solution for the )" + name + R"( problem
+    1D NumPy float array: the best known solution for the )"
+           + name + R"( problem
 
 )";
 }
@@ -2709,7 +2710,7 @@ std::string pso_gen_docstring()
 {
     return R"(__init__(gen = 1, omega = 0.7298, eta1 = 2.05, eta2 = 2.05, max_vel = 0.5, variant = 5, neighb_type = 2, neighb_param = 4, memory = False, seed = random)
 
-Particle Swarm Optimization (generational) is identical to `pagmo::pso`, but does update the velocities of each particle before new particle positions are computed (taking
+Particle Swarm Optimization (generational) is identical to :class:`~pygmo.pso`, but does update the velocities of each particle before new particle positions are computed (taking
 into consideration all updated particle velocities). Each particle is thus evaluated on the same seed within a generation as opposed to the standard PSO which evaluates single particle 
 at a time. Consequently, the generational PSO algorithm is suited for stochastic optimization problems.
 
@@ -4683,7 +4684,8 @@ Set the seed for the ``"random"`` selection/replacement policies.
     seed (``int``): the value that will be used to seed the random number generator used by the ``"random"``
       election/replacement policies (see :attr:`~pygmo.)"
            + algo + R"(.selection` and
-      :attr:`~pygmo.)" + algo + R"(.replacement`)
+      :attr:`~pygmo.)"
+           + algo + R"(.replacement`)
 
 Raises:
     OverflowError: if the attribute is set to an integer which is negative or too large
@@ -5567,4 +5569,4 @@ Clear all numeric options.
 )";
 }
 
-} // namespace
+} // namespace pygmo