Global Class #

File position: /EMOC/src/core/global.h and /EMOC/src/core/global.cpp

class Global(const char* algorithm_name, const char* problem_name, int population_num, int dec_num, int obj_num, int max_evaluation, int thread_id, int output_interval, int run_id = 0)

Global class represents an execution entity of EMOC which basically is a specified algorithm optimized a certain problem with pre-defined parameters. It holds all the relevant datas such as the pointer to algorithm, the pointer to problem, optimization settings and the population.

Parameter:
	algorithm_name: const char, default=None* The name of the specified algorithm. problem_name: const char, default=None* The name of the specified problem. population_num: int, default=None The number of population size. dec_num: int, default=None The number of the decision variables for problem settings. obj_num: int, default=None The number of objective functions for problem settings. max_evaluation: int, default=None The max evaluation times for current run. thread_id: int, default=None The index of used thread. If there is no multi-thread enabled, 0 will be used. output_interval: int, default=None The interval of population saving in generation. run_id: int, default=0 The index of current run. This is for multiple runs with same parameter settings, starting from 0.
Member variables:
	**(public) dec_num_: int The number of the decision variables. It is equal to the given dec_num. (public) obj_num_: int The number of objectives. It is equal to the given obj_num. (public) population_num_: int The number of population size. It is equal to the given population_num. (public) max_evaluation_: int The max evaluation times. It is equal to the given max_evaluation. (public) output_interval_: int The interval of population saving in generation. It is equal to the given output_interval. (public) algorithm_name_: std::string The name of the specified algorithm. It is equal to the given algorithm_name. (public) problem_name_: std::string The name of the specified problem. It is equal to the given problem_name. (public) iteration_num_: int The iteration index. It is updated by the algorithm in it's optimization loop. (public) current_evaluation_: int Current evaluation times. It is updated when the individual is evaluated. (public) algorithm_: Algorithm* Pointer to the current run's algorithm object. (public) problem_: Problem* Pointer to the current run's problem object. (public) dec_lower_bound_: std::vector<double> Lower bound for each decision variables. (public) dec_upper_bound_: std::vector<double> Upper bound for each decision variables. (public) parent_population_: std::vector<Individual>* The parent population of current run. (public) offspring_population_: std::vector<Individual>* The offspring population of current run. (public) mixed_population_: std::vector<Individual>*** The mixed population of current run. It is usually used to do some merge work.

Public Methods:

void Init()
void Start()
void InitializeIndividual(Individual* ind)
void InitializePopulation(Individual** pop, int pop_num)

void Init()

Initialize the Global object such as setting the parameters’ value and allocating memories.

Parameter:
	void
Returns:
	void

void Start()

Start the optimization with current configuration.

Parameter:
	void
Returns:
	void

void InitializeIndividual(Individual* ind)

Initialize the given individual (i.e. set the decision variables with uniform random number in the decision bounds).

Parameter:
	ind: Individual , default=None* The pointer to the individual which need to be initialized.
Returns:
	void

void InitializePopulation(Individual** pop, int pop_num)

Initialize the given population.

Parameter:
	pop: Individual, default=None The population which need to be initialized. It's an array of Individual* where each Individual* is a pointer to a individual in the population. pop_num: int, default=None The size of the given population.
Returns:
	void