scholarly journals Enhanced Tunicate Swarm Algorithm for Solving Large-Scale Nonlinear Optimization Problems

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
O. Saleh ◽  
Enas A. Hagag ◽  
Abd Allah A. Mousa
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Wilcoxon Test ◽  
Traditional Methods ◽  
Cpu Time ◽  
Statistical Measures ◽  
Searching Strategy ◽  
Swarm Algorithm ◽  
Benchmark Test Functions

AbstractNowadays optimization problems become difficult and complex, traditional methods become inefficient to reach global optimal solutions. Meanwhile, a huge number of meta-heuristic algorithms have been suggested to overcome the shortcomings of traditional methods. Tunicate Swarm Algorithm (TSA) is a new biologically inspired meta-heuristic optimization algorithm which mimics jet propulsion and swarm intelligence during the searching for a food source. In this paper, we suggested an enhancement to TSA, named Enhanced Tunicate Swarm Algorithm (ETSA), based on a novel searching strategy to improve the exploration and exploitation abilities. The proposed ETSA is applied to 20 unimodal, multimodal and fixed dimensional benchmark test functions and compared with other algorithms. The statistical measures, error analysis and the Wilcoxon test have affirmed the robustness and effectiveness of the ETSA. Furthermore, the scalability of the ETSA is confirmed using high dimensions and results exhibited that the ETSA is least affected by increasing the dimensions. Additionally, the CPU time of the proposed algorithms are obtained, the ETSA provides less CPU time than the others for most functions. Finally, the proposed algorithm is applied at one of the important electrical applications, Economic Dispatch Problem, and the results affirmed its applicability to deal with practical optimization tasks.

scholarly journals Improved Fitness-Dependent Optimizer Algorithm

2020 ◽  
Author(s):  
Danial A. Muhammed ◽  
Soran AM. Saeed ◽  
Tarik A. Rashid
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Standard Test ◽  
Particle Swarm Algorithm ◽  
Test Function ◽  
Dragonfly Algorithm ◽  
Whale Optimization ◽  
Real World Applications ◽  
Swarm Algorithm ◽  
Benchmark Test Functions

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

scholarly journals A Survey on Meta-heuristic Algorithms for Global Optimization Problems

2020 ◽  
pp. 48-60
Author(s):  
Abdel Nasser H. Zaied ◽  
Mahmoud Ismail ◽  
Salwa El-Sayed ◽  
◽  
◽  
...  
Keyword(s):  
Global Optimization ◽  
Real World ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Traditional Methods ◽  
Complex Problems ◽  
First Choice ◽  
Important Field ◽  
Real World Problems

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

scholarly journals A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yuqi Fan ◽  
Junpeng Shao ◽  
Guitao Sun ◽  
Xuan Shao
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Iteration Process ◽  
Function Optimization ◽  
Search Performance ◽  
Global Function ◽  
Salp Swarm Algorithm ◽  
Search Speed ◽  
Swarm Algorithm ◽  
Feasible Solutions

Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2022 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid ◽  
Abeer Alsadoon ◽  
Nebojsa Bacanin ◽  
Polla Fattah ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Scale Optimization ◽  
Benchmark Test Functions

<p></p><p></p><p>The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><p></p><p></p>

scholarly journals Improved Fitness-Dependent Optimizer Algorithm

2020 ◽  
Author(s):  
Danial A. Muhammed ◽  
Soran AM. Saeed ◽  
Tarik A. Rashid
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Standard Test ◽  
Particle Swarm Algorithm ◽  
Test Function ◽  
Dragonfly Algorithm ◽  
Whale Optimization ◽  
Real World Applications ◽  
Swarm Algorithm ◽  
Benchmark Test Functions

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

scholarly journals Alpha-T: Learning to Traverse over Graphs with An AlphaZero-inspired Self-Play Framework

2021 ◽  
Author(s):  
Qi Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimal Path ◽  
Solution Space ◽  
Approximate Solutions ◽  
Small Scale ◽  
Traditional Methods ◽  
Routing Problem ◽  
Monte Carlo Tree Search ◽  
Combinatorial Optimization Problems

Abstract The combinatorial optimization problems on the graph are the core and classic problems in artificial intelligence and operations research. For example, the Vehicle Routing Problem (VRP) and Traveling Salesman Problem (TSP) are not only very interesting NP-hard problems but also have important significance for the actual transportation system. Traditional methods such as heuristics methods, precise algorithms, and solution solvers can already find approximate solutions on small-scale graphs. However, they are helpless for large-scale graphs and other problems with similar structures. Moreover, traditional methods often require artificially designed heuristic functions to assist decision-making. In recent years, more and more work has focused on the application of deep learning and reinforcement learning (RL) to learn heuristics, which allows us to learn the internal structure of the graph end-to-end and find the optimal path under the guidance of heuristic rules, but most of these still need manual assistance, and the RL method used has the problems of low sampling efficiency and small searchable space. In this paper, we propose a novel framework (called Alpha-T) based on AlphaZero, which does not require expert experience or label data but is trained through self-play. We divide the learning into two stages: in the first stage we employ graph attention network (GAT) and GRU to learn node representations and memory history trajectories, and in the second stage we employ Monte Carlo tree search (MCTS) and deep RL to search the solution space and train the model.

scholarly journals A Novel Approach Based on Crow Search Algorithm for Solving Reactive Power Dispatch Problem

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3321 ◽  
Author(s):  
Asma Meddeb ◽  
Nesrine Amor ◽  
Mohamed Abbes ◽  
Souad Chebbi
Keyword(s):  
Large Scale ◽  
Reactive Power ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Standard Test ◽  
Total Power ◽  
Power Dispatch ◽  
Novel Approach ◽  
Reactive Power Dispatch

This paper presents a novel meta-heuristic approach based on the crow search algorithm (CSA) for solving the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear optimization problem designed to minimize power losses while satisfying the required constraints. The CSA is a recent efficient approach that depends on the intelligent behavior of crows. Nowadays, it has been used to solve many complex engineering optimization problems where it has proven its power and effectiveness. Motivated by the high ability in solving complex optimization problems and faster convergence of CSA, this paper proposes a novel approach to solve the ORPD problem. Furthermore, the settings of control variables such as generator terminal voltage, tap changer positions, and capacitor banks are determined to achieve the minimum total power loss while satisfying a set of nonlinear constraints. The accuracy and the performance of the proposed algorithm were performed and compared to other meta-heuristic algorithms reported in the literature. Several tests are applied on two standard test systems, including IEEE 14-bus and IEEE 30-bus as well as on the large-scale Tunisian 86-bus system. In addition, a sensitivity analysis has been performed to valid the performance of the CSA in solving the ORPD problem. We demonstrate that the proposed CSA provides a supremacy results and statistically significant in solving ORPD problems (for IEEE-14 bus p < 0.0006 , for IEEE-30 bus p < 0.006 , and for Tunisian 86-bus p < 0.0000001 ).

scholarly journals Improved Fitness-Dependent Optimizer Algorithm

2020 ◽  
Author(s):  
Danial A. Muhammed ◽  
Soran AM. Saeed ◽  
Tarik A. Rashid
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Standard Test ◽  
Particle Swarm Algorithm ◽  
Test Function ◽  
Dragonfly Algorithm ◽  
Whale Optimization ◽  
Real World Applications ◽  
Swarm Algorithm ◽  
Benchmark Test Functions

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&amp;rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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