Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

Algorithm 1

(1)	Procedure Structured Clanning-based Ensemble Optimization
(2)	Initialize of groups of agents with each group containing 1 female leader, child agents, female agents and bounded male agents. D is dimension of the search space.
(3)	Initialize semi bounded agents and assign a random group to it.
(4)	Initialize unbounded agents.
(5)	Total agents
(6)	Find Fitness of all the agents.
(7)	for Stopping criteria not met do
(8)	Assign best fitness position in each group to the Local Leader .
(9)	Assign best position to the Global Female Leader
(10)	for each do
(11)	if Agent “i” is Local Female Leader of family then
(12)	for each do
(13)	Generate random number r between 0 and 1.
(14)	if then
(15)	Update position using Equation (1)
(16)	else if Agent “i” is Bounded Agent of family then
(17)	for each do
(18)	Update position using Equation (4)
(19)	else if Agent “i” is Semibounded Agent then
(20)	for each do
(21)	Update position using Equation (7)
(22)	else if Agent “i” is Unbounded Agent then
(23)	for each do
(24)	Generate random number r between 0 and 1.
(25)	if then
(26)	Update position using Equation (10)
(27)	Update fitness value of the Agent
(28)	Update alpha value (linearly decrease)