An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems

Table 1

The parameters used in the proposed algorithm.

Parameter's name	Description	Value
𝑁	The population size	100
𝑚	Number of objectives	As the test problem
𝑛	Number of variables in a solution	As the test problem
𝑇	Maximum number of generations	250
cr	The crossover rate used in DE	0.3
𝑓	The control parameter used in DE	0.5
𝑇max	Maximum temperature in simulated annealing	100
𝑇min	Minimal temperature in simulated annealing	1𝑒−7
𝛾	The cooling rate in simulated annealing	0.6
𝑚𝑎𝑥_𝑙𝑖𝑓𝑒_𝑐𝑖𝑐𝑙𝑒	Maximum prior life cycle value of the individual	1