libmoon 0.1.11

Moon, Make MOO great again

Moon: A Standardized/Flexible Framework for MultiObjective OptimizatioN

'' I raise my cup to invite the moon. With my shadow we become three from one. '' -- Li Bai.

Moon: is a multiobjective optimization framework, from single-objective optimization to multiobjective optimization, towards a better understanding of optimization problems and fair comparasions between MOO algorithms.

Main contributors: Xiaoyuan Zhang (project leader), Ji Cheng, Liao Zhang, Weiduo Liao, Zhe Zhao, Xi Lin, Cheng Gong, Longcan Chen.

Advised by: Prof. Yifan Chen, Prof. Zhichao Lu, Prof. Ke Shang, Prof. Tao Qin.

Corresponding to: Prof. Qingfu Zhang (CityU HK).

(1) A standardlized gradient based framework.

• Problem class. For more problem details, please also check the Readme_problem.md file. (i) For synthetic problems,

• Problem	Paper	Project/Code
  ZDT	paper	project
  DTLZ	[paper]	project
  MAF	paper	[project]
  WFG code	Real world problems.	Y
  Fi's code	Real world problems.	Y
  RE	paper	code

(2) For multitask learning problems,

Problem	Paper	Project/Code
MO-MNISTs	PMTL	COSMOS
Fairness Classification	COSMOS	COSMOS
Federated Learning

(3) For MORL problems,

Problem	Paper	Project/Code
Synthetic (DST FTS...)	Envelop	code
Robotics (MO-MuJoCo...)	PGMORL	code

• Gradient-based Solver.

  Method	Property	#Obj	Support	Published	Complexity
  EPO code	Exact solution.	Any	Y	ICML 2020	$O(m^2 n K )$
  COSMOS code	Approximated exact solution.	Any	Y	ICDM 2021	$O(m n K )$
  MOO-SVGD code	A set of diverse Pareto solution.	Any	Y	NeurIPS 2021	$O(m^2 n K^2 )$
  MGDA code	Arbitray Pareto solutions. Location affected highly by initialization.	Any	Y	NeurIPS 2018	$O(m^2 n K )$
  PMTL code	Pareto solutions in sectors.	2. 3 is difficult.	Y	NeurIPS 2019	$O(m^2 n K^2 )$
  PMGDA	Pareto solutions satisfying any preference.	Any	Y	Under review	$O(m^2 n K )$
  GradienHV WangHao code	It is a gradient-based HV method.	2/3	Y	CEC 2023	$O(m^2 n K^2 )$
  Aggregation fun. based, e.g. Tche,mTche,LS,PBI,...	Pareto solution with aggregations.	Any	Y

  Here, $m$ is the number of objectives, $K$ is the number of samples, and $n$ is the number of decision variables. For neural network based methods, $n$ is the number of parameters; hence $n$ is very large (>10000), K is also large (e.g., 20-50), while $m$ is small (2.g., 2-4).

  As a result, m^2 is not a big problem. n^2 is a big problem. K^2 is a big problem.

  Time complexity of gradient based methods are as follows, -1 Tier 1. GradAggSolver. -2 Tier 2. MGDASolver, EPOSolver, PMTLSolver. -3 Tier 3. GradHVSolver -4 Tier 4. MOOSVGDSolver

  Current support: GradAggSolver, MGDASolver, EPOSolver, MOOSVGDSolver, GradHVSolver, PMTLSolver.

  Important things to notice: The original code MOO-SVGD does not offer a MTL implement. Our code is the first open source code for MTL MOO-SVGD.

• PSL solvers

  • EPO-based
  • Agg-based
  • Hypernetwork-based
  • ConditionalNet-based
  • Simple PSL model
  • Generative PSL model

• MOEA/D Current supported:

  • Vanilla MOEA/D

  • Will be released soon:

  • MOEA/D AWA.

  • MOEA/D neural AWA.

• ML pretrained methods.

  • HV net (https://arxiv.org/abs/2203.02185).

Example code:

from libmoon.solver.gradient import GradAggSolver
from libmoon.util_global.constant import problem_dict
from libmoon.util_global.weight_factor.funs import uniform_pref
import torch
import numpy as np
from matplotlib import pyplot as plt
import argparse
from libmoon.visulization.view_res import vedio_res

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='example')
    parser.add_argument('--n-partition', type=int, default=10)
    parser.add_argument('--agg', type=str, default='tche')  # If solve is agg, then choose a specific agg method.
    parser.add_argument('--solver', type=str, default='agg')
    parser.add_argument('--problem-name', type=str, default='VLMOP2')
    parser.add_argument('--iter', type=int, default=1000)
    parser.add_argument('--step-size', type=float, default=1e-2)
    parser.add_argument('--tol', type=float, default=1e-6)
    args = parser.parse_args()
    
    
    # Init the solver, problem and prefs. 
    solver = GradAggSolver(args.step_size, args.iter, args.tol)
    problem = problem_dict[args.problem_name]
    prefs = uniform_pref(args.n_partition, problem.n_obj, clip_eps=1e-2)
    args.n_prob = len(prefs)

    # Initialize the initial solution 
    if 'lbound' in dir(problem):
        if args.problem_name == 'VLMOP1':
            x0 = torch.rand(args.n_prob, problem.n_var) * 2 / np.sqrt(problem.n_var) - 1 / np.sqrt(problem.n_var)
        else:
            x0 = torch.rand(args.n_prob, problem.n_var)
    else:
        x0 = torch.rand( args.n_prob, problem.n_var )*20 - 10


    # Solve results
    res = solver.solve(problem, x=x0, prefs=prefs, args=args)
    
    # Visualize results
    y_arr = res['y']
    plt.scatter(y_arr[:,0], y_arr[:,1], s=50)
    plt.xlabel('$f_1$', fontsize=20)
    plt.ylabel('$f_2$', fontsize=20)
    plt.show()
    
    # If use vedio
    use_vedio=True
    if use_vedio:
        vedio_res(res, problem, prefs, args)