Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

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Optimal operation of reservoir systems using the Wolf Search Algorithm (WSA)

Water Science & Technology Water Supply ◽

10.2166/ws.2019.005 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1396-1404 ◽

Cited By ~ 4

Author(s):

Edris Ahmadebrahimpour

Keyword(s):

Genetic Algorithm ◽

Water Resources Management ◽

Simple Structure ◽

Search Algorithm ◽

Global Optimum ◽

Optimal Operation ◽

Resources Management ◽

Reservoir System ◽

Hydropower Plants ◽

Reservoir Systems

Abstract Optimizing hydropower plants is complex due to nonlinearity, complexity, and multidimensionality. This study introduces and evaluates the performance of the Wolf Search Algorithm (WSA) for optimizing the operation of a four-reservoir system and a single hydropower system in Iran. Results indicate WSA could reach 99.95 and 99.91 percent of the global optimum for the four-reservoir system and single reservoir system, respectively. Comparing the results of WSA with a genetic algorithm (GA) also indicates WSA's supremacy over GA. Thus, due to its simple structure and high capability, WSA is recommended for use in other water resources management problems.

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Optimal operation of multi-reservoir systems: comparative study of three robust metaheuristic algorithms

Water Science & Technology Water Supply ◽

10.2166/ws.2020.368 ◽

2020 ◽

Author(s):

Saeid Akbarifard ◽

Mohammad Reza Sharifi ◽

Kourosh Qaderi ◽

Mohamad Reza Madadi

Keyword(s):

Harmony Search ◽

Imperialist Competitive Algorithm ◽

Complex Problem ◽

Global Optimum ◽

Optimal Operation ◽

Metaheuristic Algorithms ◽

Reservoir System ◽

Optimal Value ◽

Reservoir Systems ◽

Cpu Usage

Abstract In this study, the capability of recently introduced Moth Swarm Algorithm (MSA) was compared with two robust meta-heuristics of harmony search (HS) algorithm and imperialist competitive algorithm (ICA). First, the performance of these algorithms was assessed by seven benchmark functions having 2–30 dimensions. Next, they were compared in optimization of complex problem of 4-reservoir and 10-reservoir systems operation. Furthermore, the results of these algorithms were compared with nine other metaheuristic algorithms developed by several researchers. Sensitivity analysis was performed to determine the appropriate values of the algorithms parameters. The statistical indices of R2, RMSE, MAE, MSE, NMSE, MAPE, and Willmott's index of agreement were used to compare the algorithms performance. The results showed that the MSA was the superior algorithm in solving all benchmark functions in terms of obtaining the optimal value and saving the CPU usage. ICA and HS were placed in the next orders, respectively. It was found that by increasing the dimensions of the problem, the performance of ICA and HS dropped but the MSA has still performed extraordinary. In addition, the minimum CPU usage and the best solutions for optimal operation of four-reservoir system were obtained by MSA algorithm with values of (269.7s and 308.83) which are very close to the global optimum solution. Corresponding values for ICA and HS were (486.73, 306.47) and (638.61s, 264.61) respectively, which put them in the next ranks. Similar results were observed for ten-reservoir system; the CPU time and optimal value obtained by MSA were (722.5s, 1,195.58) while for ICA and HS were (1,421.62s, 1,136.22) and (1,963.41s, 1,060.76), respectively. The values of R2 and RMSE achieved by MSA were (0.951, 0.528) and (0.985, 0.521) for 4-reservoir and 10-reservoir systems which demonstrated the outstanding performance of this algorithm in optimal operation of multi-reservoir systems. In a general comparison, it was concluded that among the twelve investigated algorithms, MSA was the best, and it is recommended as a robust promising tool in optimal operation of multi-reservoir systems.

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Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

Scientific Reports ◽

10.1038/s41598-021-95159-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mohammad Reza Sharifi ◽

Saeid Akbarifard ◽

Kourosh Qaderi ◽

Mohamad Reza Madadi

Keyword(s):

Objective Function ◽

Optimization Algorithm ◽

Optimal Operation ◽

Engineering Problem ◽

Performance Criteria ◽

Reliable Technique ◽

Reservoir System ◽

Dam Reservoirs ◽

Reservoir Systems ◽

Swarm Algorithm

AbstractDeriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.

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Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

Energy Reports ◽

10.1016/j.egyr.2021.06.008 ◽

2021 ◽

Vol 7 ◽

pp. 3703-3725

Author(s):

Mohammad Ehteram ◽

Fatemeh Barzegari Banadkooki ◽

Chow Ming Fai ◽

Mohsen Moslemzadeh ◽

Michelle Sapitang ◽

...

Keyword(s):

Power Generation ◽

Optimization Algorithm ◽

Optimal Operation ◽

Reservoir Systems

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Artificial Life Optimization Algorithm and Applications

Handbook of Research on Artificial Immune Systems and Natural Computing ◽

10.4018/978-1-60566-310-4.ch020 ◽

2011 ◽

pp. 423-449

Author(s):

Bo-Suk Yang

Keyword(s):

Genetic Algorithm ◽

Optimization Algorithm ◽

Artificial Life ◽

Optimization Problem ◽

Global Optimum ◽

Function Optimization ◽

Global Function ◽

Test Function ◽

Optimum Solution ◽

Artificial World

This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global function optimization problem. In the ALRT, the emergent colony is a fundamental mechanism to search the optimum solution and can be accomplished through the metabolism, movement and reproduction among artificial organisms which appear at the optimum locations in the artificial world. In this case, the optimum locations mean the optimum solutions in the optimization problem. Hence, the ALRT focuses on the searching for the optimum solution in the location of emergent colonies and can achieve more accurate global optimum. The optimization results using different types of test functions are presented to demonstrate the described approach successfully achieves optimum performance. The algorithm is also applied to the test function optimization and optimum design of short journal bearing as a practical application. The optimized results are compared with those of genetic algorithm and successive quadratic programming to identify the optimizing ability.

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A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

Mathematical Problems in Engineering ◽

10.1155/2015/769245 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 13

Author(s):

Lijin Wang ◽

Yiwen Zhong ◽

Yilong Yin ◽

Wenting Zhao ◽

Binqing Wang ◽

...

Keyword(s):

Differential Evolution ◽

Optimization Algorithm ◽

Iteration Process ◽

Global Optimum ◽

Backtracking Search ◽

De Algorithm ◽

Search Optimization ◽

Backtracking Search Optimization Algorithm ◽

Mutation Strategies ◽

Multimodal Problems

The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

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Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm

Water Resources Management ◽

10.1007/s11269-020-02583-8 ◽

2020 ◽

Vol 34 (10) ◽

pp. 3099-3112

Author(s):

Yousif H. Al-Aqeeli ◽

Omar M. A Mahmood Agha

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Optimal Operation ◽

Swarm Optimization ◽

Reservoir System ◽

Hydropower Production

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Energy Demand Forecast of City Based on Cellular Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2122 ◽

2012 ◽

Vol 263-266 ◽

pp. 2122-2125

Author(s):

Yu Gui Cheng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Bp Neural Network ◽

Energy Demand ◽

The Other ◽

Demand Forecast ◽

Minimum Error ◽

Search Optimization ◽

Cellular Genetic Algorithm ◽

Better Than

As a branch of genetic algorithm (GA), cellular genetic algorithm (CGA) has been used in search optimization of the population in recent years. Compared with traditional genetic algorithm and the algorithm combined with traditional genetic algorithm and BP neural network, energy demand forecast of city by the method of combining cellular genetic algorithm and BP neural network had the characteristic of the minimum training times, the shortest consumption time and the minimum error. Meanwhile, it was better than the other two algorithms from the point of fitting effect.

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