Evolutionary simplex adaptive Hooke-Jeeves algorithm for economic load dispatch problem considering valve point loading effects

Tabassum, Muhammad Farhan; Saeed, Muhammad; Chaudhry, Nazir Ahmad; Ali, Javaid; Farman, Muhammad; Akram, Sana

doi:10.1016/j.asej.2020.04.006

Cited by 34 publications

(14 citation statements)

References 88 publications

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“…The proposed iHS was evaluated using five real-world ELD test cases and substantiated to be robust and effective in solving non-convex ELD problems with impressive results. A solution to non-convex ELD problems with equality constraints using a Hybrid Genetic Algorithm (HGA) with modified Hooke and Jeeves method is presented in [11]. The proposed HGA is assessed compared against several state-of-the-art methods and demonstrated robustness, reliability, good convergence property and generation cost to produce a better quality solution.…”

Section: Previous Methods Of Eldmentioning

confidence: 99%

“…The majority of MH methods applied in solving ELD problems are either modified versions or hybrids. Such modified MH algorithms include island-based harmony search algorithm [10], evolutionary simplex adaptive Hooke-Jeeves algorithm [11], multi-group marine predator algorithm [12], modified Krill Herd Algorithm (KHA) [13], and many more. Such hybrid MH algorithms include hybrid salp swarm algorithm [14], hybrid Grey Wolf Op-timizer (GWO) [15], and others.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

Braik

Awadallah

Al-Betar

et al. 2022

Preprint

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Economic Load Dispatch (ELD) is an important task in power systems that is concerned with scheduling a set of thermal generating units to produce specific power at low consumption costs. In this task, there are some operational constraints related to power demand, power output, valve-point loading effect, transmission losses, prohibited operating zones, and ramp-rate limits. ELD is drafted as a non-linear, non-convex, and highly constrained optimization problem with a large number of local minima. Due to the complex and rugged search space of this problem, it has been tackled by many meta-heuristic algorithms. To cope with the above issues of this problem, we presented a novel Improved based Hybrid Capuchin Search Algorithm (IHCSA). First, a memory element was added to CSA to consolidate its position and velocity updating mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive functions rather than stationary social and cognitive parameters were used to manage and adequately balance exploration and exploitation of features over the iterations path. Finally, the hybridization was carried out using Gradient Search Algorithm (GBO) and Local Escaping Capability (LEO) to strengthen the intensification ability of CSA and balance its search capabilities to fulfill robust search performance. The proficiency of IHCSA was divulged by assessing it on a computationally difficult ELD problem under 6 different tests with different numbers of generators and loading conditions. The proposed IHCSA provided the best performance, in terms of the minimum cost, among the basic CSA and other optimization methods.

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Section: Previous Methods Of Eldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

Braik

Awadallah

Al-Betar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…At each step, the stop criterion is checked and if the exploratory move leads to the optimum point, the search is carried out by performing the pattern move; otherwise, the process is reset. In general, the exploratory move intends to achieve useful information regarding the objective function in the neighborhood of the current base point, while the pattern move identifies the best search direction by using the acquired information [54]. This method of optimization is similar to univariate search; therefore, its convergence speed is quite slow, might fail depending on the initial conditions, and it is very sensitive to the parameters' variations.…”

Section: Hooke and Jeeves' Methods And Powell's Methodsmentioning

confidence: 99%

Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

et al. 2021

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Energy generation and its utilization is bound to increase in the following years resulting in accelerating depletion of fossil fuels, and consequently, undeniable damages to our environment. Over the past decade, despite significant efforts in renewable energy realization and developments for electricity generation, carbon dioxide emissions have been increasing rapidly. This is due to the fact that there is a need to go beyond the power sector and target energy generation in an integrated manner. In this regard, energy systems integration is a concept that looks into how different energy systems, or forms, can connect together in order to provide value for consumers and producers. Cogeneration and trigeneration are the two most well established technologies that are capable of producing two or three different forms of energy simultaneously within a single system. Integrated energy systems make for a very strong proposition since it results in energy saving, fuel diversification, and supply of cleaner energy. Optimization of such systems can be carried out using several techniques with regards to different objective functions. In this study, a variety of optimization methods that provides the possibility of performance improvements, with or without presence of constraints, are demonstrated, pinpointing the characteristics of each method along with detailed statistical reports. In this context, optimization techniques are classified into two primary groups including unconstrained optimization and constrained optimization techniques. Further, the potential applications of evolutionary computing in optimization of Integrated Energy Systems (IESs), particularly Combined Heat and Power (CHP) and Combined Cooling, Heating, and Power (CCHP), utilizing renewable energy sources are grasped and reviewed thoroughly. It was illustrated that the employment of classical optimization methods is fading out, replacing with evolutionary computing techniques. Amongst modern heuristic algorithms, each method has contributed more to a certain application; while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization (PSO) was mostly applied for economic improvements. Given the mathematical nature and constraint satisfaction property of Mixed-Integer Linear Programming (MILP), this method is gaining prominence for scheduling applications in energy systems.

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“…Generally speaking, the combinatorial optimization problems are NP-hard. When the problem scale is small, accurate algorithms are adopted to obtain exact solutions step by step, for example, branch-and-cut approach (Lin and Tian, 2021) and Hooke-Jeeves pattern search (Tabassum et al, 2021). However, these methods mainly take exponential time to run.…”

Section: Introductionmentioning

confidence: 99%

A hybrid discrete state transition algorithm for combinatorial optimization problems

Jiang

Shen

2023

Front. Energy Res.

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The discrete state transition algorithm (DSTA) has been wildly applied to deal with combinatorial optimization problems. However, its low convergence accuracy limits its application in large-scale optimization problems. Aiming at the convergence performance and search intensity of the algorithm, a hybrid discrete state transition algorithm (HDSTA) is proposed in this work by introducing tabu search and elite solution set. Firstly, a searching mechanism with the integration of DSTA and tabu search (TS) is established, which allows moving to adjacent solutions at an increased cost to escape from the local optimum. Specifically, a tabu list as adaptive memory is adopted to avoid the loop when deviating from local optima. Secondly, an elite solution set is introduced to integrate the information of the previous optimal solution and the global optimal solution, and the search strategy is modified to expand the range and diversity of candidate solutions. Finally, the proposed HDSTA is verified according to the real data on two well-known optimization problems (staff assignment problem and traveling salesman problem) and the real data of an industrial case. The experimental results show the effectiveness of the proposed algorithm in large-scale optimization problems.

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Evolutionary simplex adaptive Hooke-Jeeves algorithm for economic load dispatch problem considering valve point loading effects

Cited by 34 publications

References 88 publications

A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

A hybrid discrete state transition algorithm for combinatorial optimization problems

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