This is a simple metaheuristic library design for class

auto instance = MH::Trajectory::Instance<Encoding>();

• Encoding is the type of the encoded solution.

• instance.generationLimit : Limit of search generations.
• prototype uint64_t generationLimit;
• instance.neighborhood : neighbourhood generator.
• prototype std::vector<Encoding> (*neighbourhood)(Encoding &);
• instance.evaluate : solution fitness evaluator.
• prototype double (*evaluate)(Encoding &, void *inf);
• instance.inf : additional information provide to evaluator.
• prototype void *inf;

ItrativeImprovement (II) :

auto II = MH::Trajectory::II<SelectionStrategy>();

or use the aliasMH::Trajectory::ItrativeImprovement

• SelectionStrategyis the seleciton strategy of II.
• MH::Trajectory::II_BestImproving
• MH::Trajecotry::II_FirstImproving

Simulated Annealing (SA) :

auto SA = MH::Trajectory::SA();

or use the aliasMH::Trajectory::SimulatedAnnealing

TabuSearch (TS) :

auto TS = MH::Trajectory::TS<Encoding, TraitType>();

or use the aliasMH::Trajectory::TabuSearch

• Encoding is the type of the encoded solution.
• TraitTypeis the type of the trait that will be stored in the tabu list.

II : None

SA :

• SA.epoch_length : epoch length.
• uint64_t epoch_length;
• SA.init_temperature : initial temperature.
• double init_temperature;
• SA.cooling : cooling function.
• double (*cooling)(double);

TS :

• TS.length : tabu tenure.
• uint8_t length;
• TS.trait : trait convert function.
• TraitType (*trait)(Encoding&, void *);
• The second parameter is instance.inf

instance.search(algortihm, init_solution);

auto instance = MH::Evolutionary::Instance<Encoding>();

• Encoding is the type of the encoded solution.

• instance.generationLimit : limit of search generation。
• prototype uint64_t generationLimit;
• instance.neighborhood : neighbourhood generator.
• prototype std::vector<Encoding> (*neighbourhood)(Encoding &);
• instance.evaluate : solution fitness evaluator.
• prototype double (*evaluate)(Encoding &, void *inf);
• instance.inf : additional information provide to evaluator.
• void *inf;

Differential Evolution(DE) :

auto DE = MH::Evolutionary::DE<Selection, Crossover>();

or use the alias MH::Evolutionary::DifferentialEvolution

• Selectionis the selection strategy of DE.
• MH::Evolutionary::DE_Random
• MH::Evolutionary::DE_Best
• MH::Evolutionary::DE_CurrentToRandom
• MH::Evolutionary::DE_CurrentToBest
• Crossoveris the crossover strategy of DE.
• MH::Evolutionary::DE_Binomial
• MH::Evolutionary::DE_Exponential

DE :

• DE.crossover_rate : crossover rate, should be in the interval (0, 1].
• double crossover_rate;
• DE.current_factor : scaling factor of xToCurrent policy.
• Only required for DE_CurrentToRandom or DE_CurrentToBest.
• double current_factor;
• DE.scaling_factor : scaling factor of mutant vector.
• double scaling_factor;
• DE.num_of_diff_vectors : the number of mutant vectors to be generated。
• uint8_t num_of_diff_vectors;

instance.evolution(algorithm, init_solution)

About Encoding: should provide the operator==().