Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem

Trivedi, Anupam; Srinivasan, Dipti; Biswas, Subhodip; Reindl, Thomas

doi:10.1016/j.swevo.2015.04.001

Cited by 97 publications

(37 citation statements)

References 65 publications

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“…Equation (24) means that the features of solution are changed by solution H i and solution H e . At first, the new solution is more affected by another better solution which accelerates the convergence characteristics of the algorithm.…”

Section: Migration Operator For Ssd Modelmentioning

confidence: 99%

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Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

2017

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Abstract:The portfolio optimization problem is the central problem of modern economics and decision theory; there is the Mean-Variance Model and Stochastic Dominance Model for solving this problem. In this paper, based on the second order stochastic dominance constraints, we propose the improved biogeography-based optimization algorithm to optimize the portfolio, which we called εBBO. In order to test the computing power of εBBO, we carry out two numerical experiments in several kinds of constraints. In experiment 1, comparing the Stochastic Approximation (SA) method with the Level Function (LF) algorithm and Genetic Algorithm (GA), we get a similar optimal solution by εBBO in [0, 0.6] and [0, 1] constraints with the return of 1.174% and 1.178%. In [−1, 2] constraint, we get the optimal return of 1.3043% by εBBO, while the return of SA and LF is 1.23% and 1.26%. In experiment 2, we get the optimal return of 0.1325% and 0.3197% by εBBO in [0, 0.1] and [−0.05, 0.15] constraints. As a comparison, the return of FTSE100 Index portfolio is 0.0937%. The results prove that εBBO algorithm has great potential in the field of financial decision-making, it also shows that εBBO algorithm has a better performance in optimization problem.

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Section: Migration Operator For Ssd Modelmentioning

confidence: 99%

“…The hybridization between the BBO and Differential Evolution Algorithm (DE) has achieved many great results [24][25][26]. However, they mostly incorporate DE into the migration procedure.…”

Section: Mutation Operator For Ssd Modelmentioning

confidence: 99%

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

2017

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“…For Ther conversion of energy into electricity, several methods of optimization to resolve the problem of unit commitment (UC) have been used in the literature, including a technique of primacies list, classical mathematical programming techniques, like Lagrangian Relaxation (LR) and dynamic programming (DP) and more newly artificial intelligence (AI) techniques [12]. Although, requiring small computation time and easy to implement, the priority list technique does not guarantee an opportune resolution near the global optimal one, which implies an operation of higher cost [13,14].…”

Section: State Of the Artmentioning

confidence: 99%

Self-scheduling of Wind-Thermal Systems Using a Stochastic MILP Approach

Laia

Gomes

Pousinho

et al. 2017

IFIP Advances in Information and Communication Technology

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Abstract. In this work a stochastic (Stoc) mixed-integer linear programming (MILP) approach for the coordinated trading of a price-taker thermal (Ther) and wind power (WP) producer taking part in a day-ahead market (DAM) electricity market (EMar) is presented. Uncertainty (Uncer) on electricity price (EPr) and WP is considered through established scenarios. Thermal units (TU) are modelled by variable costs, start-up (ST-UP) technical operating constraints and costs, such as: forbidden operating zones, minimum (Min) up/down time limits and ramp up/down limits. The goal is to obtain the optimal bidding strategy (OBS) and the maximization of profit (MPro). The wind-Ther coordinated configuration (CoConf) is modelled and compared with the unCoConf. The CoConf and unCoConf are compared and relevant conclusions are drawn from a case study.

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“…Thermal energy conversion into electric energy has a significant state of art on optimization methods for solving the thermal scheduling problem (ScP), ranging from the old priorities list method to the traditional mathematical methods up to the more lately reported artificial intelligence methods [10]. The priority list method is easily implemented and requires a small processing time, but does not guarantee an appropriate solution near the global optimal one [11].…”

Section: State Of the Artmentioning

confidence: 99%

Optimal Bidding Strategies of Wind-Thermal Power Producers

Laia

Pousinho

Melício

et al. 2016

Technological Innovation for Cyber-Physical Systems

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Abstract. This paper addresses a stochastic mixed-integer linear programming model for solving the self-scheduling problem of a thermal and wind power producer acting in an electricity market. Uncertainty on market prices and on wind power is modelled via a scenarios set. The mathematical formulation of thermal units takes into account variable and start-up costs and operational constraints like: ramp up/down limits and minimum up/down time limits. A mixed-integer linear formulation is used to obtain the offering strategies of the coordinated production of thermal and wind energy generation, aiming the profit maximization. Finally, a case study is presented and results are discussed.

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Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem

Cited by 97 publications

References 65 publications

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

Self-scheduling of Wind-Thermal Systems Using a Stochastic MILP Approach

Optimal Bidding Strategies of Wind-Thermal Power Producers

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