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 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.

scholarly journals An adaptive gravitational search algorithm for global optimization

2019 ◽  
Vol 16 (2) ◽  
pp. 724
Author(s):  
Ying-Ying Koay ◽  
Jian-Ding Tan ◽  
Chin-Wai Lim ◽  
Siaw-Paw Koh ◽  
Sieh-Kiong Tiong ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Population Based ◽  
Adaptive Search ◽  
Step Size ◽  
Search Mechanism ◽  
Gravitational Search

<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification

2020 ◽  
pp. 74-99 ◽  
Author(s):  
Sajad Ahmad Rather ◽  
P. Shanthi Bala
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Classification Problems ◽  
Local Optima ◽  
Clustering And Classification

In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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 An Improved Electromagnetic Field Optimization for the Global Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Alkin Yurtkuran
Keyword(s):  
Electromagnetic Field ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Golden Ratio ◽  
New Physics ◽  
Population Based ◽  
Statistical Testing ◽  
Optimal Point ◽  
Bee Colony ◽  
Solution Generation

Electromagnetic field optimization (EFO) is a relatively new physics-inspired population-based metaheuristic algorithm, which simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, the population consists of electromagnetic particles made of electromagnets corresponding to variables of an optimization problem and is divided into three fields: positive, negative, and neutral. In each iteration, a new electromagnetic particle is generated based on the attraction-repulsion forces among these electromagnetic fields, where the repulsion force helps particle to avoid the local optimal point, and the attraction force leads to find global optimal. This paper introduces an improved version of the EFO called improved electromagnetic field optimization (iEFO). Distinct from the EFO, the iEFO has two novel modifications: new solution generation function for the electromagnets and adaptive control of algorithmic parameters. In addition to these major improvements, the boundary control and randomization procedures for the newly generated electromagnets are modified. In the computational studies, the performance of the proposed iEFO is tested against original EFO, existing physics-inspired algorithms, and state-of-the-art meta-heuristic algorithms as artificial bee colony algorithm, particle swarm optimization, and differential evolution. Obtained results are verified with statistical testing, and results reveal that proposed iEFO outperforms the EFO and other considered competitor algorithms by providing better results.

An Overview on Stochastic Global Optimization and its Multi-Domain Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 1-6
Author(s):  
Venkateswarlu Chimmiri
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bee Colony ◽  
Highly Nonlinear ◽  
Evolutionary Optimization Algorithms ◽  
Discrete Optimization Problems

Optimization is of great interest and it has widespread applications in engineering and science. It has become a major technology contributor to the growth of industry. It is extensively used in solving a wide variety of problems in design, operation, and analysis of engineering and technological processes. Optimization of large-scale problems pose difficulties concerning to dimensionality, differentiability, multimodality and nonlinearity in objective functions and constraints. In order to overcome such difficulties, there has been a rapidly growing interest in advanced optimization algorithms. Stochastic and evolutionary optimization algorithms are increasingly used to solve challenging optimization problems. These algorithms include genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony algorithm, and cuckoo search algorithm. These algorithms are typically inspired by some phenomena from nature and they are robust. These algorithms do not require any gradient information and are even suitable to solve discrete optimization problems. These methods are extensively used to solve the optimization problems concerning to systems that are highly nonlinear, high dimensional, and noisy or for solving problems that are not easily solved by classical deterministic methods of optimization.

Inspirations from Nature for Meta-heuristic Algorithms: A Survey

2019 ◽  
Vol 13 ◽  
Author(s):  
Rohit Kumar Sachan ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Water Cycle ◽  
Search Algorithm ◽  
Theoretical Perspective ◽  
Gravitational Search Algorithm ◽  
Natural Phenomenon ◽  
Flower Pollination Algorithm ◽  
Shuffled Frog Leaping ◽  
Nature Inspired Algorithms

Background: Nature-Inspired Algorithms (NIAs) are the most efficient way to solve advanced engineering and real-world optimization problems. Since the last few decades, various researchers have proposed an immense number of NIAs. These NIAs get inspiration from natural phenomenon. A young researcher attempting to undertake or solve a problem using NIAs is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some scores over others. Objective: This paper presents a comprehensive study of seven NIAs, which have new and unique inspirations. This study shall useful to easily understand the fundamentals of NIAs for any new entrant. Conclusion: Here, we classify the NIAs as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, well-establish and relatively new NIAs, namely- Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), Water Cycle Algorithm (WCA), Jaya Algorithm and Anti-Predatory NIA (APNIA), have been studied. This study presents a theoretical perspective of NIAs in a simplified form based on its source of inspiration, mathematical formulations, control parameters, features, variants and area of application, where these algorithms have been successfully applied.

Harmony Search Algorithm and its Variants

2015 ◽  
Vol 29 (08) ◽  
pp. 1539001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
The Past

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

scholarly journals Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions

2019 ◽  
Vol 22 (2) ◽  
pp. 263-280 ◽  
Author(s):  
F. Soghrati ◽  
R. Moeini
Keyword(s):  
Reservoir Operation ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Gravitational Search Algorithm ◽  
Operation Time ◽  
Optimal Operation ◽  
Large Reservoir ◽  
Operation Optimization ◽  
Bee Colony

Abstract In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in order to improve the performance of proposed algorithms to solve large-scale problems, two constrained versions of these algorithms have been proposed, in which in these algorithms the problem constraints have been explicitly satisfied. Comparison of the results shows that using the proposed algorithm leads to better results with low computational costs in comparison with other available methods such as genetic algorithm (GA), standard and improved particle swarm optimization (IPSO) algorithm, honey-bees mating optimization (HBMO) algorithm, ant colony optimization algorithm (ACOA), and gravitational search algorithm (GSA). Therefore, the proposed algorithms are capable algorithms to solve large reservoir operation optimization problems.

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