diff --git a/bin/pycbc_optimize_snr b/bin/pycbc_optimize_snr
index f6c51c7b135..f0eea382558 100755
--- a/bin/pycbc_optimize_snr
+++ b/bin/pycbc_optimize_snr
@@ -130,13 +130,28 @@ def compute_minus_network_snr_pso(v, *argv, **kwargs):
     return -nsnr_array
 
 
-def optimize_di(bounds, cli_args, extra_args):
-    bounds = [
+def normalize_initial_point(initial_point, bounds):
+    return (initial_point - bounds[:,0]) / (bounds[:,1] - bounds[:,0])
+
+def optimize_di(bounds, cli_args, extra_args, initial_point):
+    bounds = numpy.array([
         bounds['mchirp'],
         bounds['eta'],
         bounds['spin1z'],
         bounds['spin2z']
-    ]
+    ])
+
+
+    # Currently only implemented for random seed initial array
+    rng = numpy.random.mtrand._rand
+    population_shape=(cli_args.di_popsize, 4)
+    population = rng.uniform(size=population_shape)
+    if cli_args.include_candidate_in_optimizer:
+        # Re-normalize the initial point into the correct range
+        point_init = normalize_initial_point(initial_point, bounds)
+        # add the initial point to the population
+        population = numpy.concatenate((population[:-1], point_init))
+
     results = differential_evolution(
         compute_minus_network_snr,
         bounds,
@@ -146,12 +161,13 @@ def optimize_di(bounds, cli_args, extra_args):
         mutation=(0.5, 1),
         recombination=0.7,
         callback=callback_func,
-        args=extra_args
+        args=extra_args,
+        init=population
     )
     return results.x
 
 
-def optimize_shgo(bounds, cli_args, extra_args):
+def optimize_shgo(bounds, cli_args, extra_args, initial_point): # pylint: disable=unused-argument
     bounds = [
         bounds['mchirp'],
         bounds['eta'],
@@ -168,30 +184,48 @@ def optimize_shgo(bounds, cli_args, extra_args):
     )
     return results.x
 
+def normalize_population(population, min_bounds, max_bounds):
+    norm_pop = min_bounds + population * (max_bounds - min_bounds)
+
+    return norm_pop
 
-def optimize_pso(bounds, cli_args, extra_args):
+def optimize_pso(bounds, cli_args, extra_args, initial_point):
     options = {
         'c1': cli_args.pso_c1,
         'c2': cli_args.pso_c2,
         'w': cli_args.pso_w
     }
-    min_bounds = [
+    min_bounds = numpy.array([
         bounds['mchirp'][0],
         bounds['eta'][0],
         bounds['spin1z'][0],
         bounds['spin2z'][0]
-    ]
-    max_bounds = [
+    ])
+    max_bounds = numpy.array([
         bounds['mchirp'][1],
         bounds['eta'][1],
         bounds['spin1z'][1],
         bounds['spin2z'][1]
-    ]
+    ])
+
+    # Manually generate the initial points, this is the same as the default
+    # method, but allows us to make some modifications
+    population = numpy.random.uniform(
+        low=0.0, high=1.0, size=(cli_args.pso_particles, 4)
+    )
+    population = normalize_population(population, min_bounds, max_bounds)
+
+    if cli_args.include_candidate_in_optimizer:
+        # add the initial point to the population
+        population = numpy.concatenate((population[:-1],
+                                        initial_point))
+
     optimizer = ps.single.GlobalBestPSO(
         n_particles=cli_args.pso_particles,
         dimensions=4,
         options=options,
-        bounds=(min_bounds, max_bounds)
+        bounds=(min_bounds, max_bounds),
+        init_pos=population
     )
     _, results = optimizer.optimize(
         compute_minus_network_snr_pso,
@@ -284,6 +318,14 @@ parser.add_argument('--pso-c2', type=float, default=2.0,
 parser.add_argument('--pso-w', type=float, default=0.01,
                     help='Only relevant for --optimizer pso: '
                     'The hyperparameter w: the inertia parameter.')
+parser.add_argument('--include-candidate-in-optimizer', action='store_true',
+                    help='Include parameters of the candidate event in the '
+                    'initialised array for the optimizer. Only relevant for '
+                    '--optimizer pso or differential_evolution')
+parser.add_argument('--seed', type=int,
+                    help='Seed to supply to the random generation of initial '
+                    'array to pass to the optimizer. Only relevant for '
+                    '--optimizer pso or differential_evolution')
 
 scheme.insert_processing_option_group(parser)
 fft.insert_fft_option_group(parser)
@@ -296,6 +338,9 @@ if args.optimizer == 'pso' and ps == None:
     parser.error('You need to install pyswarms to use the pso optimizer.')
 pycbc.init_logging(args.verbose)
 
+if args.seed:
+    numpy.random.seed(args.seed)
+
 scheme.verify_processing_options(args, parser)
 fft.verify_fft_options(args, parser)
 
@@ -375,11 +420,27 @@ bounds = {
     'spin2z': (minspin2z, maxspin2z)
 }
 
+if args.include_candidate_in_optimizer:
+    # Initial point from found candidate
+    mchirp_init = cv.mchirp_from_mass1_mass2(fp['mass1'][()], fp['mass2'][()])
+    eta_init = cv.eta_from_mass1_mass2(fp['mass1'][()], fp['mass2'][()])
+    spin1z_init = fp['spin1z'][()]
+    spin2z_init = fp['spin2z'][()]
+
+    initial_point = numpy.array([
+        mchirp_init,
+        eta_init,
+        spin1z_init,
+        spin2z_init,
+    ])[numpy.newaxis]
+else:
+    initial_point = None
+
 with scheme_context:
     logging.info('Starting optimization')
 
     optimize_func = optimize_funcs[args.optimizer]
-    opt_params = optimize_func(bounds, args, extra_args)
+    opt_params = optimize_func(bounds, args, extra_args, initial_point)
 
     logging.info('Optimization complete')