A genetic algorithm solution to the unit commitment problem

This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported     

Microgenetic algorithms as generalized hill-climbing operators forGA optimization

We investigate the potential of a microgenetic algorithm (MGA) as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the main GA, looking for better solutions. The MGA operator performs genetic local search. The major advantage of MGA is its ability to identify and follow narrow ridges of arbitrary direction leading to the global optimum. The proposed GA-MGA scheme is tested against 13 different schemes, including a simple GA and GAs with different hill-climbing operators. Experiments are conducted on a test set including eight constrained optimization problems with continuous variables. Extensive simulation results demonstrate the efficiency of the proposed GA-MGA scheme. For the same number of fitness evaluations, GA-MGA exhibited a significantly better performance in terms of solution accuracy, feasibility percentage of the attained solutions, and robustness     

Varying fitness functions in genetic algorithm constrainedoptimization: the cutting stock and unit commitment problems

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problem's constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms. The resulting varying fitness function facilitates the GA search. The performance of the technique is tested on two optimization problems: the cutting stock, and the unit commitment problems. Also, new domain-specific operators are introduced. Solutions obtained by means of the varying and the conventional (nonvarying) fitness function techniques are compared. The results show the superiority of the proposed technique.     

ACES: an interactive software platform for self-instruction and self-evaluation in automatic control systems

This paper presents an interactive-, menu-driven prototype software platform, namely automatic control educational software (ACES), for self-instruction and self-evaluation in automatic control systems. ACES is used for enriching instruction in automatic control at Aristotle University of Thessaloniki, Greece, in the Department of Electrical and Computer Engineering. The ACES platform includes theory with hyperlinks, a concept-graph, and a database with exercises. Students' answers to exercises are evaluated automatically "on-line." Furthermore, exercises can be proposed automatically by ACES. An instructor/supervisor can support in person the learning effort of a student, monitor the progress of a student, and, also, tailor a course's contents on the modular ACES platform. Two statistical hypothesis tests on both attitude questionnaires and student marks in the final (written) exam confirmed that the employment of ACES in the educational process can improve the performance of students in an automatic control course although the attitude of students toward the course does not change significantly with the use of ACES.