Random-Like Multiple Objective Decision Making
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The download will achieve trigger to your Kindle care. Because of the existence of some random parameters cNi , eNr and bNr , we cannot easily obtain its optimal solutions. Then we can obtain the following expected value model of problem 2. Assume that random vectors cNi D. Assume that for any i D 1; 2; : : : ; m, j D 1; 2; : : : ; n and r D 1; 2; : : : ; p, cNij , eNij and bNij are independently random variables. Then problem 2. Since random vector cNi is normally distributed on. Assume that random vector cNi D. Since random variables cNij are exponentially distributed on the probability space.
This completes t u We just take the normal distribution and the exponential distribution as examples, and readers can get the similar results when random parameters are subject to other distributions. If there are more than two different distributions in the same problem, readers can also deal with it by the expected value operator and convert it into the crisp one. Take the problem 2. Let eT b Hi. P Assume that the related weight of the objective function Hi.
Construct the evaluation function as follows, u. Then we get the following weight problem, max u. By changing w, we can obtain a set composed of the efficient solutions of the problem 2. As we know, it is almost impossible to convert it into a crisp one. Thus, an intelligent algorithm should be provided to solve it. The technique of stochastic simulation-based SA is a useful and efficient tool when dealing with them.
Then the procedure simulating the expected value of the function f.
Set L D 0; Step 2. Repeat the second and third steps N times; Step 5. SA are proposed by Kirkpatrick et al. The name of the algorithm derived from an analogy between the simulation of the annealing of solid first proposed by Metropolis et al. The motivation of the methods lies in the physical process of annealing, in which a solid is heated to a liquid state and, when cooled sufficiently slowly, takes up the configuration with minimal inner energy.
Metropolis et al. Simulating annealing uses this mathematical description for the minimization of other functions than the energy. For a related earlier result, see Hasminskij . Most of the early considerations concern minimization of functions defined on a finite set. Kushner  and Gelfand and Mitter  obtained results for functions with infinite domains.
Laarhoven and Aarts , and Laarhoven  are monographs on simulated annealing. Steel , in a review of , calls simulated annealing the most exciting algorithmic development of the decade. Annealing, physically, refers to the process of heating up a solid to a high temperature followed by slow cooling achieved by decreasing the temperature of the environment in steps.
At each step the temperature is maintained constant for a period of time sufficient for the solid to reach thermal equilibrium. At equilibrium, the solid could have many configurations, each corresponding to different spins of the electrons and to specific energy level. Simulated annealing is a computational stochastic technique for obtaining near global optimum solutions to combinatorial and function optimization problems. The method is inspired from the thermodynamic process of cooling annealing of molten metals to attain the lowest free energy state.
When molten metal is cooled slowly enough it tends to solidify in a structure of minimum energy. This annealing process is mimicked by a search strategy. The key principle of the method is to allow occasional worsening moves so that these can eventually help locate the neighborhood to the true global minimum. For the purpose 2. The consideration of such a probability distribution leads to the generation of a Markov chain of points in the problem domain. The acceptance criterion given by 2.
Another variant of this acceptance criterion for both improving and deteriorating moves has been proposed by Galuber  and can be written as probability. This allows us to explore solution space. Then, gradually the temperature is reduced which means that one becomes more and more selective in accepting new solution.
By the end, only the improving moves are accepted in practice. The temperature is systematically lowered using a problem-dependent schedule characterized by a set of decreasing temperatures.
Next, we introduce the general framework for the simulated annealing algorithm. The standard SA technique makes the analogy between the state of each molecule that determines the energy function and the value of each parameter that affects the objective functions. It then uses the statistical mechanics principle for energy minimization to minimize the objective function and optimize the parameter estimates. Starting with a high temperature, it randomly perturbs the parameter values and calculates the resulting objective function.
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The new state of objective function after perturbation is then accepted by a probability determined by the Metropolis criterion. The system temperature is then gradually reduced as the random perturbation proceeds, until the objective function reaches its global or nearly global minimum. A typical SA algorithm is described as follows Fig. Under kth temperature, if the inner loop break condition is met, go to step 3; otherwise, for. Reduce Tk to TkC1 following a specified cooling schedule.
If outer loop break condition is met, computation stops and optimal parameter set is reached; if not, return back to step 2. The steps outlined above consist of one inner loop step 2 and one outer loop step 3. The proceeding of SA is mainly controlled by 1 the choice of T0 ; 2 the way a new perturbation is generated; 3 the inner loop break conditions; 4 the choice of cooling schedule; and 5 the outer loop break conditions.
The pseudocode can be seen in Table 2.presskit.pockettroops.com
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For the multiobjective optimization problem, many researchers have proposed many kinds of SA algorithms to obtain the Pareto-optimal solutions. Suppapitnarm et al. They use the objective function values but not the weight vector as an acceptance criterion after penalizing them and annealing temperature and consider multiple annealing temperatures usually one per objective. Therefore, the key probability step can be given as: 2. On the other hand, the penalty function approach can help us to convert the constrained problem to an unconstrained one.
Above all, the SA algorithm which is proposed to solve the multiobjective programming problem m objective functions and n decision variables by Suppapitnarm et al.
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Randomly generate a feasible x by random simulation and put x into a Pareto set of solutions. Compute all objective values; Step 2. Generate a new solution y in the neighborhood of x by the random perturbation. Compute the objective values and apply the penalty function approach to the corresponding objective functions, if necessary; Step 3. Compare the generated solution with all solutions in the Pareto set and update the Pareto set if necessary; Step 4.
Replace the current solution x with the generated solution y if y is archived and go to Step 7; Step 5. Accept the generated solution y as the current solution if it is not archived with the probability: probability. If the generated solution is accepted, replace x with y and go to Step 7; Step 6. If the generated solution as current solution vector by x D x and go to Step 7; Step 7. Periodically, restart with a randomly selected solution from the Pareto set. While periodically restarting with the archived solutions, Suppapitnarm et al.
Periodically reduce the temperature using a problem-dependent annealing schedule; Step 9. Repeat steps 2—8, until a predefined number of iterations is carried out. There are also many other SA algorithms designed to solve multiobjective programming problems by many scholars.
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For example, Ulungu et al. Suman [, ] proposed the WMOSA algorithm to handle constraints with its main algorithm by using weight vector in the acceptance criterion. Suman  also proposed the PDMOSA algorithm which uses the fitness value in the acceptance criteria to handle the multiobjective optimization problems. We consider only the random simulation-based SMOSA in this book and readers can find more detail about the multiobjective optimization problem solved by SA in .