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Non dominated sorting particle swarm optimization(NSPSO)
===========================================================
.. versionadded:: 2.12
.. cpp:namespace-push:: pagmo
.. cpp:class:: nspso
Non-dominated Sorting Particle Swarm Optimizer (NSPSO) is a modified version of PSO for multi-objective optimization.
It extends the basic ideas of PSO by making a better use of personal bests and offspring for non-dominated comparison. In order to increase the diversity of the Pareto front it is possible to choose between 3 different niching methods: crowding distance, niche count and maxmin.|
* See: Xiadong, Li. "A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization". Genetic and Evolutionary Computation - GECCO (2003), vol. 2723, pp. 37-48, doi: https://doi.org/10.1007/3-540-45105-6_4.
* See: Xiadong, Li. "Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function". Genetic and Evolutionary Computation - GECCO (2004), vol. 3102, pp. 117-128, doi: https://doi.org/10.1007/978-3-540-24854-5_11.
* See: Fonseca, M. Carlos and Fleming, J. Peter. "Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization". Proceedings of the ICGA-93: Fifth International Conference on Genetic Algorithms (1993), vol. 3, pp. 416-423.
This constructor will construct NSPSO.
.. cpp:function:: nspso(unsigned gen = 1u, double omega = 0.6, double c1 = 2.0, double c2 = 2.0, double chi = 1.0, double v_coeff = 0.5, unsigned leader_selection_range = 60u, std::string diversity_mechanism = "crowding distance", bool memory = false, unsigned seed = pagmo::random_device::next())
:param `gen`: number of generations to evolve.
:param `omega`: particles' inertia weight.
:param `c1`: magnitude of the force, applied to the particle's velocity, in the direction of its previous best position.
:param `c2`: magnitude of the force, applied to the particle's velocity, in the direction of its global best (i.e., leader).
:param `chi`: velocity scaling factor.
:param `v_coeff`: velocity coefficient (determining the maximum allowed particle velocity).
:param `leader_selection_range`: leader selection range parameter (i.e., the leader of each particle is selected among the best `leader_selection_range` % `individuals`).
:param `diversity_mechanism`: the diversity mechanism used to maintain diversity on the Pareto front.
:param `memory`: memory parameter. If `true`, memory is activated in the algorithm for multiple calls.
:param `seed`: seed used by the internal random number generator (default is random).
:exception `std\:\:invalid_argument`: if `c1` <= 0, or `c2` <= 0, or `chi` <= 0.
:exception `std\:\:invalid_argument`: if `omega` < 0, or `omega` > 1,.
:exception `std\:\:invalid_argument`: if `v_coeff` <= 0, or `v_coeff` > 1.
:exception `std\:\:invalid_argument`: if `leader_selection_range` > 100.
:exception `std\:\:invalid_argument`: if `diversity_mechanism` is not "*crowding distance*", or "*niche count*", or "*max min*".
.. cpp:function:: population evolve(population pop) const
Algorithm evolve method: evolves the population for the requested number of generations.
:param pop: population to be evolved.
:return: evolved population.
:throw: ``std::invalid_argument`` if ``pop.get_problem()`` is stochastic, single objective or has non linear constraints. If the population size is smaller than 2.
.. cpp:function:: void set_seed(unsigned seed)
Sets the seed.
:param ``seed``: the seed controlling the algorithm stochastic behaviour.
.. cpp:function:: unsigned get_seed(unsigned seed)
Gets the seed.
:return: the seed controlling the algorithm stochastic behaviour.
.. cpp:function:: void set_verbosity(unsigned level)
Sets the algorithm verbosity: sets the verbosity level
of the screen output and of the log returned by ``get_log()``.
*level* can be:
* 0: no verbosity.
* >0: will print and log one line each *level* generations.
Example (verbosity 1, where Gen, is the generation number,
Fevals the number of function evaluations used; also, the
ideal point of the current population follows cropped to its
5th component):
.. code-block:: c++
:linenos:
Gen: Fevals: ideal1: ideal2: ideal3: ideal4: ideal5: ... :
1 52 0.0586347 0.0587097 0.0586892 0.0592426 0.0614239
2 104 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
3 156 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
4 208 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
5 260 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
6 312 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
7 364 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
8 416 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
9 468 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
10 520 0.00899252 0.00899395 0.00945782 0.0106282 0.0276778
.. cpp:function:: unsigned get_verbosity() const
Gets the verbosity level.
:return: the verbosity level.
.. cpp:function:: unsigned get_gen() const
Gets the generations.
:return: the number of generations to evolve for.
.. cpp:function:: const log_type &get_log() const
Gets the log. A log containing relevant quantities monitoring the last call to evolve. Each element of the returned ``std::vector`` is a ``nspso::log_line_type`` containing: Gen, Fevals, ideal_point as described in ``nspso::set_verbosity``.
:return: an ``std::vector`` of ``nspso::log_line_type`` containing the logged values Gen, Fevals, ideal_point.
.. cpp:function:: void set_bfe(const bfe &b)
Sets the batch function evaluation scheme.
:param ``b``: batch function evaluation object.
.. cpp:function:: std::string get_extra_info() const
Extra info. Returns extra information on the algorithm.
:return: an ``std::string`` containing extra info on the algorithm.
.. cpp:function:: std::string get_name() const
Returns the problem name.
:return: a string containing the problem name: "NSPSO".
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