scholarly journals Crow Search Algorithm (CSA)

2019 ◽  
Vol 8 (3S2) ◽  
pp. 371-375
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Metaheuristic Algorithms ◽  
Heuristic Optimization ◽  
Keen Interest ◽  
Diverse Application ◽  
Complex Building

Researchers’ are taking keen interest in Optimization algorithms due to their heuristic and meta-heuristic nature. Heuristic algorithms find the arrangement by utilizing the experimentation strategy. Then again, meta-heuristic algorithms discover the response at a more elevated tier. Several nature-based metaheuristic algorithms are easily accessible. Askarzadeh has introduced the Crow search algorithm and stated that it is meta-heuristic optimization algorithm. The astute conduct of the crow moves CSA. Crows are keen on putting away the abundance nourishment at concealing spots and recuperating it at whatever point it is needed. CSA's previous outcomes show that it can unravel different complex building related optimization issues. There are six compelled building plan issues, and CSA is utilized to upgrade these issues. This paper focuses on a far-reaching investigation of CSA in the diverse application is given with the examination just as the exhibitions of the CSA in the different structure is talked about.

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’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

Meta-heuristic optimization algorithms for simultaneous optimization of sidelobe level and directivity of uniformly excited concentric ring array antennas

2019 ◽  
Vol 12 (2) ◽  
pp. 183-192
Author(s):  
Kailash Pati Dutta ◽  
G. K. Mahanti
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Beam Width ◽  
Simultaneous Optimization ◽  
Heuristic Optimization ◽  
The Novel ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Array Antennas ◽  
Symbiotic Organism Search

AbstractThis paper proposes the novel application of three meta-heuristic optimization algorithms namely crow search algorithm, moth flame optimization, and symbiotic organism search algorithm for the synthesis of uniformly excited multiple concentric ring array antennas. The objective of this work is to minimize the sidelobe level (SLL) and maximize the peak directivity simultaneously. Three different cases are demonstrated separately with various constraints such as optimal inter-element spacing and/or optimal ring radii. Comparative study of the algorithms using common parameters such as SLL, directivity, first null beam width, best cost, and run time has been reported. Investigation results prove the superiority of case 3 over other cases in terms of directivity and SLL. This work demonstrates the potential of these algorithms.

scholarly journals Economic and emission dispatch using cuckoo search algorithm

2019 ◽  
Vol 9 (5) ◽  
pp. 3384
Author(s):  
Rachid Habachi ◽  
Abdellah Boulal ◽  
Achraf Touil ◽  
Abdelkabir Charkaoui ◽  
Abdelwahed Echchatbi
Keyword(s):  
Brood Parasitism ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Energy Systems ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Cuckoo Search Algorithm ◽  
Heuristic Optimization ◽  
Generator System ◽  
Emission Dispatch

<p class="Default">The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes the use of Cuckoo Search Algorithm (CSA) for solving the economic and Emission dispatch. The effectiveness of the proposed approach has been tested on 3 generator system. CSA is a new meta-heuristic optimization method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species.The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature</p>

Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm

2018 ◽  
Vol 7 (4.6) ◽  
pp. 275
Author(s):  
Chandrasekhara Reddy T ◽  
Srivani V ◽  
A. Mallikarjuna Reddy ◽  
G. Vishnu Murthy
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Suite ◽  
Swarm Optimization ◽  
Cuckoo Algorithm

For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our proposed technique test cases are optimized by utilizing Improved Cuckoo Algorithm (ICSA). At that point, the advanced experiments are organized or prioritized by utilizing Particle Swarm Optimization algorithm (PSO). The Particle Swarm Optimization and Improved Cuckoo Algorithm (PSOICSA) estimation is a blend of Improved Cuckoo Search Algorithm(ICSA) and Particle Swarm Optimization (PSO). PSOICSA could be utilized to advance the test suite, and coordinate both ICSA and PSO for a superior outcome, when contrasted with their individual execution as far as experiment improvement. 

Comparison of Optimization Algorithms for a Tubular Aspiration Robot for Maximum Coverage in Intracerebral Hemorrhage Evacuation

2017 ◽  
Vol 02 (01) ◽  
pp. 1750004 ◽  
Author(s):  
Josephine Granna ◽  
Yi Guo ◽  
Kyle D. Weaver ◽  
Jessica Burgner-Kahrs
Keyword(s):  
Intracerebral Hemorrhage ◽  
Optimization Algorithm ◽  
Experimental Evaluation ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Exhaustive Search ◽  
Simulated Patient ◽  
Coordinated Motion ◽  
Maximum Coverage ◽  
The Brain

Intracerebral hemorrhage evacuation (ICH) using a tubular aspiration robot promises benefits over conventional approaches to release the pressure of an hemorrhage within the brain. The blood of the hemorrhage is evacuated through preplanned, coordinated motion of a flexible, curved, concentric tube that aspirates from within the hemorrhage. To achieve maximum decompression, the curvature of the inner aspirator tube has to be selected such that its workspace covers the hemorrhage. As the use of multiple aspiration tubes sequentially is advisable, one can perform an exhaustive search over all possible aspiration tube shapes as has been previously proposed in the literature. In this paper, we introduce a new optimization algorithm which is computationally more efficient and thus allows for quick optimization during surgery. To demonstrate its performance and compare it to the previously proposed exhaustive search algorithm, we present experimental evaluation results on 175 simulated patient trials.

scholarly journals Identification of Dynamic Models by Using Metaheuristic Algorithms

2021 ◽  
Vol 3 (1) ◽  
pp. 36-58
Author(s):  
Mustafa Danaci ◽  
Fehim Koylu ◽  
Zaid Ali Al-Sumaidaee
Keyword(s):  
Optimization Algorithm ◽  
Dynamic Models ◽  
Heuristic Algorithms ◽  
Data Availability ◽  
Metaheuristic Algorithms ◽  
Ant Colony Optimization Algorithm ◽  
Structural Dynamic ◽  
Bee Colony ◽  
Local Searches ◽  
Noise Rate

A modified versions of metaheuristic algorithms are presented to compare their performance in identifying the structural dynamic systems. Genetic algorithm, biogeography based optimization algorithm, ant colony optimization algorithm and artificial bee colony algorithm are heuristic algorithms that have robustness and ease of implementation with simple structure. Different algorithms were selected some from evolution algorithms and other from swarm algorithms   to boost the equilibrium of global searches and local searches, to compare the performance and investigate the applicability of proposed algorithms to system identification; three cases are suggested under different conditions concerning data availability, different noise rate and previous familiarity of parameters. Simulation results show these proposed algorithms produce excellent parameter estimation, even with little measurements and a high noise rate.

Short-Term Hydrothermal Scheduling Based on Harmony Search Algorithm

2014 ◽  
Vol 1044-1045 ◽  
pp. 1507-1510 ◽  
Author(s):  
Ping Ren ◽  
Nan Li
Keyword(s):  
Power Systems ◽  
Transmission Lines ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Time ◽  
Mathematical Optimization ◽  
Short Term ◽  
Hydrothermal Scheduling

In this paper, the nonlinear optimal control problem is formulated as a multi-objective mathematical optimization problem. Harmony search (HS) algorithm is one of the new heuristic algorithms. The harmony search (HS) optimization algorithm is introduced for the first time in solving the optimal short-term hydrothermal scheduling in power systems. A case on consisting of 9 buses, 11 transmission lines, four thermal plants and three hydro plants in Indian utility system is presented to show the methodology’s feasibility and efficiency. Compared with the optimal short-term hydrothermal scheduling of power systems, the search time of the HS optimization algorithm is shorter and the result is close to the ideal solution.

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