Self-adaptive Differential Evolution (DE 1220 aka pDE)#

class de1220#

A Differential Evolution Algorithm (1220, or pDE: our own DE flavour!!)


Differential Evolution (pagmo::de, pagmo::sade) is one of the best meta-heuristics in PaGMO, so we dared to propose our own algorithmic variant we call DE 1220 (a.k.a. pDE as in pagmo DE). Our variant makes use of the pagmo::sade adaptation schemes for CR and F and adds self-adaptation for the mutation variant. The only parameter left to be specified is thus population size.

Similarly to what done in pagmo::sade for F and CR, in DE 1220 each individual chromosome (index \(i\)) is augmented also with an integer \(V_i\) that specifies the mutation variant used to produce the next trial individual. Right before mutating the chromosome the value of \(V_i\) is adapted according to the equation:

\[\begin{split} V_i = \left\{\begin{array}{ll} random & r_i < \tau \\ V_i & \mbox{otherwise} \end{array}\right. \end{split}\]

where \(\tau\) is set to be 0.1, \(random\) selects a random mutation variant and \(r_i\) is a random uniformly distributed number in [0, 1]

See also

pagmo::de, pagmo::sade For other available algorithms based on Differential Evolution


The feasibility correction, that is the correction applied to an allele when some mutation puts it outside the allowed box-bounds, is here done by creating a random number in the bounds.


The search range is defined relative to the box-bounds. Hence, unbounded problems will produce an error.

Public Types

typedef std::tuple<unsigned, unsigned long long, double, double, double, unsigned, double, double> log_line_type#

Single entry of the log (gen, fevals, best, F, CR, Variant, dx, df)

typedef std::vector<log_line_type> log_type#

The log.

Public Functions

de1220(unsigned gen = 1u, std::vector<unsigned> allowed_variants = de1220_statics<void>::allowed_variants, unsigned variant_adptv = 1u, double ftol = 1e-6, double xtol = 1e-6, bool memory = false, unsigned seed = pagmo::random_device::next())#


Constructs pDE (a.k.a. DE 1220)

The same two self-adaptation variants used in pagmo::sade are used to self-adapt the CR and F parameters:

1 - jDE (Brest et al.)                       2 - iDE (Elsayed at al.)

A subset of the following mutation variants is considered when adapting the mutation variant:

1 - best/1/exp                               2. - rand/1/exp
3 - rand-to-best/1/exp                       4. - best/2/exp
5 - rand/2/exp                               6. - best/1/bin
7 - rand/1/bin                               8. - rand-to-best/1/bin
9 - best/2/bin                               10. - rand/2/bin
11. - rand/3/exp                             12. - rand/3/bin
13. - best/3/exp                             14. - best/3/bin
15. - rand-to-current/2/exp                  16. - rand-to-current/2/bin
17. - rand-to-best-and-current/2/exp         18. - rand-to-best-and-current/2/bin

The first ten are the classical variants introduced in the original DE algorithm, the remaining ones are, instead, introduced in the work by Elsayed et al.

See: (jDE) - Brest, J., Greiner, S., Bošković, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. Evolutionary Computation, IEEE Transactions on, 10(6), 646-657. Chicago See: (iDE) - Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2011, June). Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems. In Evolutionary Computation (CEC), 2011 IEEE Congress on (pp. 1041-1048). IEEE.

  • gen – number of generations.

  • allowed_variants – the subset of mutation variants to be considered (default is {2u ,3u ,7u ,10u ,13u ,14u ,15u ,16u})

  • variant_adptv – parameter adaptation scheme to be used (one of 1..2)

  • ftol – stopping criteria on the x tolerance (default is 1e-6)

  • xtol – stopping criteria on the f tolerance (default is 1e-6)

  • memory – when true the parameters CR anf F are not reset between successive calls to the evolve method

  • seed – seed used by the internal random number generator (default is random)

  • std::invalid_argument – if variant_adptv is not in [0,1]

  • std::invalid_argument – if allowed_variants contains a number not in 1..18

population evolve(population) const#

Algorithm evolve method.

Evolves the population for a maximum number of generations, until one of tolerances set on the population flatness (x_tol, f_tol) are met.


pop – population to be evolved

  • std::invalid_argument – if the problem is multi-objective or constrained or stochastic

  • std::invalid_argument – if the population size is not at least 7


evolved population

void set_seed(unsigned)#

Sets the seed.


seed – the seed controlling the algorithm stochastic behaviour

inline unsigned get_seed() const#

Gets the seed.


the seed controlling the algorithm stochastic behaviour

inline void set_verbosity(unsigned level)#

Sets the algorithm verbosity.

Sets the verbosity level of the screen output and of the log returned by get_log(). level can be:

  • 0: no verbosity

  • >0: will print and log one line each level generations.

Example (verbosity 1):

Gen:        Fevals:          Best:             F:            CR:       Variant:            dx:            df:
    1             15        45.4245       0.480391       0.567908              4        10.9413        35061.1
    2             30        45.4245       0.480391       0.567908              4        10.9413        35061.1
    3             45        45.4245       0.480391       0.567908              4        10.9413        35061.1
    4             60        6.55036       0.194324      0.0732594              6        9.35874        4105.24
    5             75        6.55036       0.194324      0.0732594              6        6.57553         3558.4
    6             90        2.43304       0.448999       0.678681             14        3.71972        1026.26
    7            105        2.43304       0.448999       0.678681             14        11.3925        820.816
    8            120        1.61794       0.194324      0.0732594              6        11.0693        821.631
    9            135        1.61794       0.194324      0.0732594              6        11.0693        821.631
   10            150        1.61794       0.194324      0.0732594              6        11.0693        821.631
   11            165       0.643149       0.388876       0.680573              7        11.2983        822.606
Gen, is the generation number, Fevals the number of function evaluation used, Best is the best fitness function currently in the population, F is the F used to create the best so far, CR the CR used to create the best so far, Variant is the mutation variant used to create the best so far, dx is the population flatness evaluated as the distance between the decisions vector of the best and of the worst individual and df is the population flatness evaluated as the distance between the fitness of the best and of the worst individual.


level – verbosity level

inline unsigned get_verbosity() const#

Gets the verbosity level.


the verbosity level

inline unsigned get_gen() const#

Gets the generations.


the number of generations to evolve for

inline std::string get_name() const#

Algorithm name.

One of the optional methods of any user-defined algorithm (UDA).


a string containing the algorithm name

std::string get_extra_info() const#

Extra info.

One of the optional methods of any user-defined algorithm (UDA).


a string containing extra info on the algorithm

inline const log_type &get_log() const#

Get log.

A log containing relevant quantities monitoring the last call to evolve. Each element of the returned std::vector is a de1220::log_line_type containing: Gen, Fevals, Best, F, CR, Variant, dx, df as described in de1220::set_verbosity


an std::vector of de1220::log_line_type containing the logged values Gen, Fevals, Best, F, CR, Variant, dx, df

template<typename T>
struct de1220_statics#

Static variables used in pagmo::de1220.

Public Static Attributes

static const std::vector<unsigned> allowed_variants = {2u, 3u, 7u, 10u, 13u, 14u, 15u, 16u}#

Allowed mutation variants considered by default: {2u ,3u ,7u ,10u ,13u ,14u ,15u ,16u}.