46,037 research outputs found
An intelligent novel tripartite - (PSO-GA-SA) optimization strategy
A solution approach for many challenging and non-differentiable optimization tasks in industries is the use of non-deterministic meta-heuristic methods. Some of these approaches include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). However, with the implementation usage of these robust and stochastic optimization approaches, there are still some predominant issues such as the problem of the potential solution being trapped in a local minima solution space. Other challenges include the untimely convergence and the slow rate of arriving at optimal solutions. In this research study, a tripartite version (PSO-GA-SA) is proposed to address these deficiencies. This algorithm is designed with the full exploration of all the capabilities of PSO, GA and SA functioning simultaneously with a high level of intelligent system techniques to exploit and exchange relevant population traits in real time without compromising the computational time. The design algorithm further incorporates a variable velocity component that introduces random intelligence depending on the fitness performance from one generation to the other. The robust design is validated with known mathematical test function models. There are substantial performance improvements when the novel PSO-GA-SA approach is subjected to three test functions used as case studies. The results obtained indicate that the new approach performs better than the individual methods from the fitness function deviation point of view and in terms of the total simulation time whilst operating with both a reduced number of generations and populations. Moreover, the new novel approach offers more beneficial trade-off between exploration and exploitation of PSO, GA and SA. This novel design is implemented using an object oriented programming approach and it is expected to be compatible with a variety of practical problems with specified input-output pairs coupled with constraints and limitations on the available resources
Parameter selection for genetic algorithm-based simulation optimization
Cataloged from PDF version of article.Improvements on heuristic techniques with the availability of faster PC’s increase the
importance of simulation-optimization (sim/opt) applications. Sim/opt methodologies use
computer simulation integrated with an optimization sub-routine to optimize the problems of
interest. The main contribution of these methods is to make simulation as a prescriptive tool
rather than a descriptive tool, which has been widely used as the descriptive tool for
estimating the performance of complex stochastic systems. Sim/opt methodologies have been
applied on various combinatorial optimization problems, and the current trend in sim/opt area
is the use of meta-heuristic techniques. Genetic Algorithm (GA) is the well known metaheuristic,
which is a global search algorithm taking its inspiration from natural genetics. GA
has several parameters affecting its performance. Even for the GA with same structural
parameters (coding scheme, operator types, stopping criterion), the different combinations of
numerical parameters (initial population type, population size, maximum generation number
and the crossover and mutation probabilities) may lead to drastic changes in the performance
of the algorithm. This study examines the effects of numerical parameters of GA on its
performance in terms of both fitness and CPU time; and proposes guidelines for appropriate
parameter selection. A test problem of a serial assembly line taken from the literature is used
for the GA-based simulation-optimization application. A genetic algorithm coded in C is
integrated with a simulation model developed using SIMAN simulation language.
Modifications on the test problem are made to analyze the behavior of GA parameters under
different experimental conditions. The computational results reveal that in the case of a
dominant set of decision variables, for rapid convergent GA applications high mutation rates
are more useful, whereas the crossover operator does not have any significant impact on GA
performance.Boyabatlı, OnurM.S
A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems
This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem
Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering
This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems
Comparative Studies on Decentralized Multiloop PID Controller Design Using Evolutionary Algorithms
Decentralized PID controllers have been designed in this paper for
simultaneous tracking of individual process variables in multivariable systems
under step reference input. The controller design framework takes into account
the minimization of a weighted sum of Integral of Time multiplied Squared Error
(ITSE) and Integral of Squared Controller Output (ISCO) so as to balance the
overall tracking errors for the process variables and required variation in the
corresponding manipulated variables. Decentralized PID gains are tuned using
three popular Evolutionary Algorithms (EAs) viz. Genetic Algorithm (GA),
Evolutionary Strategy (ES) and Cultural Algorithm (CA). Credible simulation
comparisons have been reported for four benchmark 2x2 multivariable processes.Comment: 6 pages, 9 figure
A comparative study of adaptive mutation operators for metaheuristics
Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs. Many researchers are applying adaptive techniques to guide the search of GAs toward optimum solutions. Mutation is a key component of GAs. It is a variation operator to create diversity for GAs. This paper investigates
several adaptive mutation operators, including population level adaptive mutation operators and gene level adaptive mutation operators, for GAs and compares their performance based on a set of uni-modal and multi-modal benchmark problems. The experimental results show that the gene
level adaptive mutation operators are usually more efficient than the population level adaptive mutation operators for GAs
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