You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: mopt_mutator.rs
    path of file: ./repos/LibAFL/libafl/src/mutators
    
    the code of the file until where you have to start completion: //! The `MOpt` mutator scheduler, see <https://github.com/puppet-meteor/MOpt-AFL> and <https://www.usenix.org/conference/usenixsecurity19/presentation/lyu>
use alloc::{
    string::{String, ToString},
    vec::Vec,
};
use core::{
    fmt::{self, Debug},
    marker::PhantomData,
};

use libafl_bolts::{
    rands::{Rand, StdRand},
    Named,
};
use serde::{Deserialize, Serialize};

use super::MutationId;
use crate::{
    corpus::{Corpus, CorpusId},
    mutators::{ComposedByMutations, MutationResult, Mutator, MutatorsTuple, ScheduledMutator},
    state::{HasCorpus, HasMetadata, HasRand, HasSolutions},
    Error,
};

/// A Struct for managing MOpt-mutator parameters.
/// There are 2 modes for `MOpt` scheduler, the core fuzzing mode and the pilot fuzzing mode.
/// In short, in the pilot fuzzing mode, the fuzzer employs several `swarms` to compute the probability to choose the mutation operator.
/// On the other hand, in the core fuzzing mode, the fuzzer chooses the best `swarms`, which was determined during the pilot fuzzing mode, to compute the probability to choose the operation operator.
/// With the current implementation we are always in the pacemaker fuzzing mode.
#[derive(Serialize, Deserialize, Clone)]
#[cfg_attr(
    any(not(feature = "serdeany_autoreg"), miri),
    allow(clippy::unsafe_derive_deserialize)
)] // for SerdeAny
pub struct MOpt {
    /// Random number generator
    pub rand: StdRand,
    /// The number of total findings (unique crashes and unique interesting paths). This is equivalent to `state.corpus().count() + state.solutions().count()`;
    pub total_finds: usize,
    /// The number of finds before until last swarm.
    pub finds_until_last_swarm: usize,
    /// These w_* and g_* values are the coefficients for updating variables according to the PSO algorithms
    pub w_init: f64,
    /// These w_* and g_* values are the coefficients for updating variables according to the PSO algorithms
    pub w_end: f64,
    /// These w_* and g_* values are the coefficients for updating variables according to the PSO algorithms
    pub w_now: f64,
    /// These w_* and g_* values are the coefficients for updating variables according to the PSO algorithms
    pub g_now: f64,
    /// These w_* and g_* values are the coefficients for updating variables according 
    
    pub fn pso_update(&mut self) -> Result<(), Error> {
        self.g_now += 1.0;
        if self.g_now > self.g_max {
            self.g_now = 0.0;
        }
        self.w_now =
            ((self.w_init - self.w_end) * (self.g_max - self.g_now) / self.g_max) + self.w_end;

        let mut operator_finds_sum = 0;

        for i in 0..self.operator_num {
            self.operator_finds_puppet[i] = self.core_operator_finds[i];

            for j in 0..self.swarm_num {
                self.operator_finds_puppet[i] += self.pilot_operator_finds[j][i];
            }
            operator_finds_sum += self.operator_finds_puppet[i];
        }

        for i in 0..self.operator_num {
            if self.operator_finds_puppet[i] > 0 {
                self.g_best[i] =
                    (self.operator_finds_puppet[i] as f64) / (operator_finds_sum as f64);
            }
        }

        for swarm in 0..self.swarm_num {
            let mut x_sum = 0.0;
            for i in 0..self.operator_num {
                self.probability_now[swarm][i] = 0.0;
                self.v_now[swarm][i] = self.w_now * self.v_now[swarm][i]
                    + (self.rand.below(1000) as f64)
                        * 0.001
                        * (self.l_best[swarm][i] - self.x_now[swarm][i])
                    + (self.rand.below(1000) as f64)
                        * 0.001
                        * (self.g_best[i] - self.x_now[swarm][i]);
                self.x_now[swarm][i] += self.v_now[swarm][i];

                self.x_now[swarm][i] = self.x_now[swarm][i].clamp(V_MIN, V_MAX);

                x_sum += self.x_now[swarm][i];
            }

            for i in 0..self.operator_num {
                self.x_now[swarm][i] /= x_sum;
                if i == 0 {
                    self.probability_now[swarm][i] = self.x_now[swarm][i];
                } else {
                    self.probability_now[swarm][i] =
                        self.probability_now[swarm][i - 1] + self.x_now[swarm][i];
                }
            }
            if self.probability_now[swarm][self.operator_num - 1] < 0.99
                || self.probability_now[swarm][self.operator_num - 1] > 1.01
            {
                return Err(Error::illegal_state(
                    "MOpt: Error in pso_update".to_string(),
                ));
            }
        }
        self.swarm_now = 0;

        // After pso_update, go back to pilot-fuzzing module
        Ok(())
    }