.. _tabusearch-py:


#############
tabusearch.py
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SIMO metaheuristic TABUSEARCH algorithm::

    >>> epsilon = 0.00001


****************************
class TabuSearch(Optimizer):
****************************

TabuSearch heuristic optimization algorithm implementation. Initialize with keyword arguments eg. maxiterations=1000, entrytenure=10, exittenure=20



def __init__(self, logger, logname, optlogger, taskdef, simdbin, opdbin, simdbout, opdbout, \*\*keywords):
==========================================================================================================

Initialize optimizer::

    >>> from simo.optimization.tabusearch import TabuSearch
    >>> execfile('optimization/test/mocks4optimizer.py')
    >>> chart_path = 'optimization/test'

    >>> ts = TabuSearch(logger, logname, taskdef, simdbin, simdbout, 
    ...                 False, False, False, chart_path, True, 'test', True,
    ...                 keyword1='test-kw')
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        'Parameter "repeats" missing!')
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        'Parameter "initial_solutions" missing!')
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        'Parameter "maximum_iterations" missing!')
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        'Parameter "entrytenure" missing!')
    Called Logger.log_message(
        'optimization-test',
        'ERROR',
        'Parameter "exittenure" missing!')

    >>> ts = TabuSearch(logger, logname, taskdef, simdbin, simdbout, False,
    ...                 False, False, chart_path, True, 'test', True, 
    ...                 repeats=2, initial_solutions=100,
    ...                 maximum_iterations=5, entrytenure=2, exittenure=3)


def optimize(self):
===================

Run tabu search optimization algorithm


def _run_TS(self):
==================

Run TS algorithm for a single iteration/repeat

Parameters::

    iteration -- current simulated iteration as int

::

    >>> ts._stat_logger = ologger  # replace stat logger with a mock
    >>> ts._data = omatrix2  # replace data handler with another mock
    >>> ts._analyze_data()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    Called OMatrix.analyze_data(
        <Mock ... SimInputDB>,
        <bound method TabuSearch._add_info of 
        <simo.optimization.tabusearch.TabuSearch object at ...>>)
    True
    >>> ts.set_data(0)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    Called OMatrix.construct_data(0)
    True
    >>> ts._run_TS()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    Called OMatrix.solution_feasibility(array([...]))
    Called OMatrix.solution_utility(array([...]))
    Called OMatrix.compare_utilities(...)
    Called OLogger.add_round(array(...]), 1.0, 1.0)
    ...


def _update_tabu_set(self, unit, b_from, b_to):
===============================================

Add a new taboo move to the set and decrease the tenure counter for existing taboos

Parameters ::

    unit -- unit indice as int
    b_from -- from branch indice as int
    b_to -- to branch indice as int

::

    >>> ts._exit_map[...] = False
    >>> ts._entry_map[...] = False
    >>> ts._exit_timer[...] = 0
    >>> ts._entry_timer[...] = 0
    >>> ts._exit_tenure = 4
    >>> ts._entry_tenure = 2
    >>> ts._update_tabu_set(0, 0, 1)
    >>> ts._update_tabu_set(1, 2, 3)
    >>> ts._update_tabu_set(2, 2, 3)
    >>> ts._update_tabu_set(3, 0, 2)
    >>> ts._exit_map
    array([[ True, False, False, False, False, False],
           [False, False,  True, False, False, False],
           [False, False,  True, False, False, False],
           [ True, False, False, False, False, False],
           [False, False, False, False, False, False]], dtype=bool)
    >>> ts._entry_map
    array([[False, False, False, False, False, False],
           [False, False, False, False, False, False],
           [False, False, False,  True, False, False],
           [False, False,  True, False, False, False],
           [False, False, False, False, False, False]], dtype=bool)
    >>> ts._exit_timer
    array([[1, 0, 0, 0, 0, 0],
           [0, 0, 2, 0, 0, 0],
           [0, 0, 3, 0, 0, 0],
           [4, 0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0, 0]])
    >>> ts._entry_timer
    array([[0, 0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0, 0],
           [0, 0, 0, 1, 0, 0],
           [0, 0, 2, 0, 0, 0],
           [0, 0, 0, 0, 0, 0]])


def _is_move_tabu(self, unit, b_from, b_to):
============================================

True if taboo, False if not

Parameters ::

    unit -- unit indice as int
    b_from -- from branch indice as int
    b_to -- to branch indice as int

::

    >>> ts._is_move_tabu(0, 0, 1)
    True
    >>> ts._is_move_tabu(1, 0, 1)
    False


def _can_exit(self, unit, b_from):
==================================

True if exit map value at (unit, frombranch) is False

Parameters ::

    unit -- unit indice as int
    b_from -- from branch indice as int

::

    >>> ts._can_exit(0,0)
    False
    >>> ts._can_exit(0,1)
    True
    >>> ts._can_exit(2,2)
    False


def _get_neighbourhood(self, unit, b_from, num_b):
==================================================

Get non-taboo neighbours of (unit, b_from)

Parameters ::

    unit -- unit indice as int
    b_from -- from branch indice as int
    num_b -- number of branches as int

::

    >>> ts._get_neighbourhood(1, 0)
    array([False,  True,  True,  True,  True,  True], dtype=bool)