Heuristic and Meta-Heuristic Optimization

Kunche, Prajna; Reddy, K. Vijaya Kumar

doi:10.1007/978-3-319-31683-3_3

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Stochastic optimization methods have proven to solve large problems finding a good solution. Stochastic algorithms are relatively easy to implement on complex problems [24]. Besides probabilistic methods, there are two types of stochastic approaches, heuristic and metaheuristic.…”

Section: Metaheuristic Approaches For Gandtep Problemmentioning

confidence: 99%

“…The implementations of metaheuristic algorithms have the ability to conduct global searches avoiding local optimal solutions [28]. There is always a trade-off between two characteristics in metaheuristic algorithms balanced in order to obtain better results: intensification and diversification [24]. Metaheuristic algorithms generally perform better than simple heuristics; they are robust and efficient [29].…”

Section: Metaheuristic Approaches For Gandtep Problemmentioning

confidence: 99%

“…The set of constraints corresponding to investment (4), (5), (6), (7), (8), (9), and (10) takes into account the physical limitations of the generating units and the budget limit. Within the group of operating constraints (11), (12), (13), (14), (15), (16), (17), (18), (19), (20), (21), (22), (23), (24), (25), (26), (27), (28), (29), and (30), (11), (12), (13), (14), and (15) state that if there are operating points with the same demand, they will have the same expansion plan in generation and transmission. Power balance constraints, thermal capacity limits in existing lines and those proposed by the model, constraints of direct current (DC) power flows for existing lines and candidates are represented in (17), (18), (19), (20), and (21), respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Power balance constraints, thermal capacity limits in existing lines and those proposed by the model, constraints of direct current (DC) power flows for existing lines and candidates are represented in (17), (18), (19), (20), and (21), respectively. The maximum generation capacity for existing and proposed units (conventional and clean) is guaranteed by (22), (23), (24), and (25). The limits on nonsupplied demand and the phase angle are described in (26), (27), and (28).…”

Section: Mathematical Modelmentioning

confidence: 99%

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Analysis of Constraint‐Handling in Metaheuristic Approaches for the Generation and Transmission Expansion Planning Problem with Renewable Energy

et al. 2018

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A multiperiod generation and transmission expansion planning (G&TEP) problem is considered. This model integrates conventional generation with renewable energy sources, assuming a stochastic approach. The proposed approach is based on a centralized planned transmission expansion. Due to the worldwide recent energy guidelines, it is necessary to generate expansion plans adequate to the forecast demand over the next years. Nowadays, in most energy systems, a public entity develops both the short and long of electricity-grid expansion planning. Due to the complexity of the problem, there are different strategies to find expansion plans that satisfy the uncertainty conditions addressed. We proposed to address the G&TEP problem with a pure genetic algorithm approach. Different constraint-handling techniques were applied to deal with two complex case studies presented. Numerical results are shown to compare the strategies used in the test systems, and key factors such as a prior initialization of population and the estimated minimum number of generations are discussed.

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Section: Metaheuristic Approaches For Gandtep Problemmentioning

confidence: 99%

Section: Metaheuristic Approaches For Gandtep Problemmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

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Analysis of Constraint‐Handling in Metaheuristic Approaches for the Generation and Transmission Expansion Planning Problem with Renewable Energy

et al. 2018

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“…This enables the ANC device to sense variations in the noise amplitude easily. A number of different algorithms have been proposed for ANC using such a dual channel set-up, (Kunche and Reddy, 2016a). The most commonly used methods are least mean-squares (LMS) and normalized LMS (NLMS) (Widrow and Stearns, 1985;Gorriz et al, 2009;Mohammed, 2007;Bai and Yin, 2010).…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization

Taha¹,

Wajid²,

Hussain³

2019

Journal of Computer Science

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Speech enhancement is used in almost all modern communication systems. This is due to the quality of speech being degraded by environmental interference factors, such as: Acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper, explores the potential of different benchmark optimization techniques for enhancing the speech signal. This is accomplished by fine tuning filter coefficients using a diverse set of adaptive filters for noise suppression in speech signals. We consider the Particle Swarm Optimization (PSO) and its variants in conjunction with the Adaptive Noise Cancellation (ANC) approach, for delivering dual speech enhancement. Comparative simulation results demonstrate the potential of an optimized coefficient ANC over a fixed one. Experiments are performed at different signal to noise ratios (SNRs), using two benchmark datasets: the NOIZEUS and Arabic dataset. The performance of the proposed algorithms is evaluated by maximising the perceptual evaluation of speech quality (PESQ) and comparing to the audio-only Wiener Filter (AW) and the Adaptive PSO for dual channel (APSOforDual) algorithms.

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