Cooperative coevolutionary algorithm for unit commitment Export

@article{citeulike:715622, abstract = {This paper presents a new cooperative coevolutionary algorithm (CCA) for power system unit commitment. CCA is an extension of the traditional genetic algorithm (GA) which appears to have considerable potential for formulating and solving more complex problems by explicitly modeling the coevolution of cooperating species. This method combines the basic ideas of Lagrangian relaxation technique (LR) and GA to form a two-level approach. The first level uses a subgradient-based stochastic optimization method to optimize Lagrangian multipliers. The second level uses GA to solve the individual unit commitment sub-problems. CCA can manage more complicated time-dependent constraints than conventional LR. Simulation results show that CCA has a good convergent property and a significant speedup over traditional GAs and can obtain high quality solutions. The "curse of dimensionality" is surmounted, and the computational burden is almost linear with the problem scale}, author = {Chen, Haoyong and Wang, Xifan}, citeulike-article-id = {715622}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=982203}, journal = {Power Systems, IEEE Transactions on}, keywords = {could-be-helpful, energy, evolutionary-programming, genetic-algorithms, lagrange-multipliers, lagrangian-relaxation, unit-commitment}, number = {1}, pages = {128--133}, posted-at = {2006-06-29 12:59:39}, priority = {0}, title = {Cooperative coevolutionary algorithm for unit commitment}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=982203}, volume = {17}, year = {2002} }

TY - JOUR ID - citeulike:715622 L3 - citeulike-article-id:715622 N2 - This paper presents a new cooperative coevolutionary algorithm (CCA) for power system unit commitment. CCA is an extension of the traditional genetic algorithm (GA) which appears to have considerable potential for formulating and solving more complex problems by explicitly modeling the coevolution of cooperating species. This method combines the basic ideas of Lagrangian relaxation technique (LR) and GA to form a two-level approach. The first level uses a subgradient-based stochastic optimization method to optimize Lagrangian multipliers. The second level uses GA to solve the individual unit commitment sub-problems. CCA can manage more complicated time-dependent constraints than conventional LR. Simulation results show that CCA has a good convergent property and a significant speedup over traditional GAs and can obtain high quality solutions. The "curse of dimensionality" is surmounted, and the computational burden is almost linear with the problem scale IS - 1 JF - Power Systems, IEEE Transactions on EP - 133 TI - Cooperative coevolutionary algorithm for unit commitment VL - 17 SP - 128 KW - could-be-helpful KW - energy KW - evolutionary-programming KW - genetic-algorithms KW - lagrange-multipliers KW - lagrangian-relaxation KW - unit-commitment AU - Chen, Haoyong AU - Wang, Xifan PY - 2002/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=982203 ER -