a J5d @sddlmZddlmZmZmZddlZddlm Z ddl m Z ddl m Z dejeeeeeeeeeeeefd d d Zejeeeeed ddZeeedddZeeeedddZdS)) default_timer)ListOptionalTupleN)neighborhood_gen)setup)compute_permutation_distancetwo_opt?F)distance_matrixx0perturbation_schemealphamax_processing_timelog_fileverbosereturncCsht||\}}t||||} |p$tj}|r8t|ddd} t|} | } d| } d}t}d}|dkr`|s`d}t| D]}t||krd}|rt|| d |rt|d }qJt ||}t ||}t ||| r||}}|d 7}d}d | d |d|d d| d|d| d|}|r,t|| d |r:t||| krvqJqv| |9} ||dk7}qZ||fS)u%Solve a TSP problem using a Simulated Annealing The approach used here is the one proposed in [1]. Parameters ---------- distance_matrix Distance matrix of shape (n x n) with the (i, j) entry indicating the distance from node i to j x0 Initial permutation. If not provided, it starts with a random path perturbation_scheme {"ps1", "ps2", "ps3", "ps4", "ps5", "ps6", ["two_opt"]} Mechanism used to generate new solutions. Defaults to "two_opt" alpha Reduction factor (``alpha`` < 1) used to reduce the temperature. As a rule of thumb, 0.99 takes longer but may return better solutions, whike 0.9 is faster but may not be as good. A good approach is to use 0.9 (as default) and if required run the returned solution with a local search. max_processing_time {None} Maximum processing time in seconds. If not provided, the method stops only when there were 3 temperature cycles with no improvement. log_file If not `None`, creates a log file with details about the whole execution verbose If true, prints algorithm status every iteration Returns ------- A permutation of nodes from 0 to n - 1 that produces the least total distance obtained (not necessarily optimal). The total distance the returned permutation produces. References ---------- [1] Dréo, Johann, et al. Metaheuristics for hard optimization: methods and case studies. Springer Science & Business Media, 2006. wzutf-8)encoding rFz/WARNING: Stopping early due to time constraints)fileTz Temperature z. Current value: z k: /z k_accepted: z k_noimprovements: ) r_initial_temperaturenpinfopenlenrrangeprint _perturbationr_acceptance_rule)r r r rrrrxfxtempZlog_file_handlernZ k_inner_minZ k_inner_maxZk_noimprovementsZticZ stop_earlyZ k_acceptedkZ warning_msgxnfnmsgr+e/var/www/html/django/DPS/env/lib/python3.9/site-packages/python_tsp/heuristics/simulated_annealing.pysolve_tsp_simulated_annealing sb7        r-)r r#r$r rc CsXg}tdD]&}t||}t||}|||q tt|}d} | t| S)uCompute initial temperature Instead of relying on problem-dependent parameters, this function estimates the temperature using the suggestion in [1]. Notes ----- Here are the steps followed: 1. Generate 100 disturbances at random from T0, and evaluate the mean objective value differences dfx_mean = mean(fn - fx); 2. Choose tau0 = 0.5 as assumed quality of initial solution (assuming a bad one), and deduce T0 from exp(-fx_mean/T0) = tau0, that is, T0 = -fx_mean/ln(tau0) References ---------- [1] Dréo, Johann, et al. Metaheuristics for hard optimization: methods and case studies. Springer Science & Business Media, 2006. dg?)rr!rappendrabsmeanlog) r r#r$r Zdfx_list_r(r)Zdfx_meanZtau0r+r+r,rws   rr#r cCstt||S)a Generate a random neighbor of a current solution ``x`` In this case, we can use the generators created in the `local_search` module, and pick the first solution. Since the neighborhood is randomized, it is the same as creating a random perturbation. )nextrr4r+r+r,r!sr!)r$r)r%rcCs6||}|dkp4|dko4tjt|| |kS)zMetropolis acceptance ruler)rrandomZrandexp)r$r)r%Zdfxr+r+r,r"s$r")Nr r NNF)ZtimeitrtypingrrrnumpyrZ*python_tsp.heuristics.perturbation_schemesrZ"python_tsp.heuristics.local_searchrZpython_tsp.utilsrZndarrayintstrfloatboolr-rr!r"r+r+r+r,s:      m '