A Hybrid Algorithm Framework with Learning and Complementary Fusion Features for Whale Optimization Algorithm

Algorithm 1

The pseudocode of hWOAlf.

	Input:
	Output:
(1)	//randU (0, 1), randperm (N)random permutation of the integers 1 : N, normrnd (u, d)random number chosen from a normal distribution with mean u and standard deviation d
(2)	Initialization for to N do
(3)	for to D do
(4)	% Generate an initial whales population
(5)	end
(6)	end
(7)	% Calculate the fitness of each search agent
(8)	= the best search agent
(9)	= the fitness of the best search agent
(10)	and
(11)	while do
(12)	Mutation
(13)	%% Update the Position of search agents
(14)	for to N do
(15)	Update a, A, C and l
(16)	if then
(17)	if then
(18)	the individual is mutated by equation (22)
(19)	else
(20)	Select a random individual ; the individual is mutated by equation (23)
(21)	end
(22)	else
(23)	if then
(24)	the individual is mutated by equation (24)
(25)	else
(26)	the individual is mutated by equation (25)
(27)	end
(28)	end
(29)	end
(30)	%% Checking allowable range
(31)	for to N do
(32)	for to D do
(33)	if or then
(34)
(35)	end
(36)	end
(37)	end
(38)	%% Update the leader
(39)	for to N do
(40)	% Calculate objective function values of T
(41)	if then
(42)
(43)
(44)	end
(45)	end
(46)	%% Update the learning parameter
(47)	the is updated by equation (26)
(48)
(49)	end