parameters: (
          self,
          gen=1,
          omega=0.7298,
          eta1=2.05,
          eta2=2.05,
          vcoeff=0.5,
          variant=5,
          neighb_type=2,
          neighb_param=4,
          nr_eval_per_x=5,
          max_fevals=10000000)
docstring: Constructs a Particle Swarm Optimization (generational). The position update is applied
      only at the end of an entire loop over the population (swarm). Use this version for stochastic problems.
  
      USAGE: algorithm.pso_gen(gen=1, omega = 0.7298, eta1 = 2.05, eta2 = 2.05, vcoeff = 0.5, variant = 5, neighb_type = 2, neighb_param = 4, nr_eval_per_x = 5, max_fevals = 10000000)
  
      * gen: number of generations
      * omega: constriction factor (or particle inertia weight) in [0,1]
      * eta1: Cognitive component in [0,4]
      * eta2: Social component in [0,4]
      * vcoeff: Maximum velocity coefficient (w.r.t. the box-bounds width) in [0,1]
      * variant: algoritmic variant to use (one of  [1 .. 6])
              1. PSO canonical (with inertia weight)
              2. PSO canonical (with inertia weight
                      and equal random weights of social and cognitive components)
              3. PSO variant (with inertia weight
                      same random number for all components.)
              4. PSO variant (with inertia weight
                      same random number for all components
                      and equal weights of social and cognitive components)
              5. PSO canonical (with constriction factor)
              6. Fully Informed Particle Swarm (FIPS)
      * neighb_type: defines the particle neighbourhood (used for the social component)
              1. gbest neighbourhood topology (fully connected)
              2. lbest neighbourhood topology (ring)
              3. Von-Neumann neighbourhood topology (square lattice)
              4. Randomly-varying neighbourhood topology
      * neighb_param: if the lbest topology is selected, it represents each particle's indegree
              (also outdegree) in the swarm topology. Particles have neighbours up
              to a radius of k = neighb_param / 2 in the ring. If the Randomly-varying neighbourhood topology
              is selected, neighb_param represents each particle's maximum outdegree in the swarm topology.
              The minimum outdegree is 1 (the particle always connects back to itself).
  * nr_eval_per_x: Specify the expected budget to be allocated during racing
      * max_fevals: When specified other than -1, this serve as another termination condition -- maximium number of objective function evaluations
docstring_summary: Constructs a Particle Swarm Optimization (generational). The position update is applied
      only at the end of an entire loop over the population (swarm). Use this version for stochastic problems.
docstring_tokens: Constructs a Particle Swarm Optimization ( generational ) . The position update is applied only at the end of an entire loop over the population ( swarm ) . Use this version for stochastic problems .
function: def _pso_gen_racing_ctor(
          self,
          gen=1,
          omega=0.7298,
          eta1=2.05,
          eta2=2.05,
          vcoeff=0.5,
          variant=5,
          neighb_type=2,
          neighb_param=4,
          nr_eval_per_x=5,
          max_fevals=10000000):
      """
      Constructs a Particle Swarm Optimization (generational). The position update is applied
      only at the end of an entire loop over the population (swarm). Use this version for stochastic problems.
  
      USAGE: algorithm.pso_gen(gen=1, omega = 0.7298, eta1 = 2.05, eta2 = 2.05, vcoeff = 0.5, variant = 5, neighb_type = 2, neighb_param = 4, nr_eval_per_x = 5, max_fevals = 10000000)
  
      * gen: number of generations
      * omega: constriction factor (or particle inertia weight) in [0,1]
      * eta1: Cognitive component in [0,4]
      * eta2: Social component in [0,4]
      * vcoeff: Maximum velocity coefficient (w.r.t. the box-bounds width) in [0,1]
      * variant: algoritmic variant to use (one of  [1 .. 6])
              1. PSO canonical (with inertia weight)
              2. PSO canonical (with inertia weight
                      and equal random weights of social and cognitive components)
              3. PSO variant (with inertia weight
                      same random number for all components.)
              4. PSO variant (with inertia weight
                      same random number for all components
                      and equal weights of social and cognitive components)
              5. PSO canonical (with constriction factor)
              6. Fully Informed Particle Swarm (FIPS)
      * neighb_type: defines the particle neighbourhood (used for the social component)
              1. gbest neighbourhood topology (fully connected)
              2. lbest neighbourhood topology (ring)
              3. Von-Neumann neighbourhood topology (square lattice)
              4. Randomly-varying neighbourhood topology
      * neighb_param: if the lbest topology is selected, it represents each particle's indegree
              (also outdegree) in the swarm topology. Particles have neighbours up
              to a radius of k = neighb_param / 2 in the ring. If the Randomly-varying neighbourhood topology
              is selected, neighb_param represents each particle's maximum outdegree in the swarm topology.
              The minimum outdegree is 1 (the particle always connects back to itself).
  * nr_eval_per_x: Specify the expected budget to be allocated during racing
      * max_fevals: When specified other than -1, this serve as another termination condition -- maximium number of objective function evaluations
      """
      # We set the defaults or the kwargs
      arg_list = []
      arg_list.append(gen)
      arg_list.append(omega)
      arg_list.append(eta1)
      arg_list.append(eta2)
      arg_list.append(vcoeff)
      arg_list.append(variant)
      arg_list.append(neighb_type)
      arg_list.append(neighb_param)
      arg_list.append(nr_eval_per_x)
      if max_fevals > 0:
          arg_list.append(max_fevals)
      self._orig_init(*arg_list)
function_tokens: def _pso_gen_racing_ctor ( self , gen = 1 , omega = 0.7298 , eta1 = 2.05 , eta2 = 2.05 , vcoeff = 0.5 , variant = 5 , neighb_type = 2 , neighb_param = 4 , nr_eval_per_x = 5 , max_fevals = 10000000 ) : # We set the defaults or the kwargs arg_list = [ ] arg_list . append ( gen ) arg_list . append ( omega ) arg_list . append ( eta1 ) arg_list . append ( eta2 ) arg_list . append ( vcoeff ) arg_list . append ( variant ) arg_list . append ( neighb_type ) arg_list . append ( neighb_param ) arg_list . append ( nr_eval_per_x ) if max_fevals > 0 : arg_list . append ( max_fevals ) self . _orig_init ( * arg_list )