Fig 1.Happiness Model
2-1 INSPIRATION
In a company, some personals attempt to obtain a new positon instead of old position, which leads causes to be happiness. By changing, the behavior of personal in the company we can modeled to an algorithm, which can be one of the specific branch of swarm intelligence.
The model by four behaviors,which mentioned before, can be make decision in the space of problem. The model should take into account the balancing exploration and exploitation in the space of problems. Four agent based on mentioned behaviorare indicated and introduced as follows
Ps H=the history of personal
Ps C= the current personal
Ps\({\ N}_{1}\)= the best neighbor of current personal
Ps \(G\) = the best personal in company or companies
The cost valuesof eachagentsare converted to the fitness value, which is defined in general equation 2. After computing, we calculate probability value for each agent, whichis given in the equation3.
\(\text{Fitness}_{i}\)=Exp (\(\frac{f_{i}}{\text{Average}(F)})\)F=\({\{f}_{1}\)\(f_{i}\ldots f_{n}\)}n=4(2)
\(P_{i}\)= \(\frac{\text{Fit}_{i}}{\sum_{i=1}^{n}\text{Fit}_{i}}\) (3)
The space of movement agents determined with twocriterionfor balancing the exploration and exploitation and scape of local minimum.
First criterion:
If the cost value of the first neighbor is less than the cost value ofthe current agentsatisfied it leads to two statesbased on the generated uniformvalue including coefficient formula. Rr1: uniformly distributed random number in the interval (0,1) (what is happen in the world) L: current iteration n: number of agents pn=n*0.75Sum=\(\sum_{i=1}^{\text{pn}}p_{i}\)(4)\(\mu_{1}\)=2-L*((2)/iteration number) (5)\(\mu_{2}\)=1-L*((1)/iteration number) (6)\(\varnothing\ \)=1-L*((1)/iteration number) (7)alpha=0.6-l*((0.6-0.09)/iteration number) (8)