scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

scholarly journals A hybrid cuckoo–harmony search algorithm for optimal design of water distribution systems

2015 ◽  
Vol 18 (3) ◽  
pp. 544-563 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Aaron C. Zecchin ◽  
Feifei Zheng ◽  
Siamak Talatahari
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Method

Meta-heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real-world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so-called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative stages of the search, followed by a thorough exploitation phase using the combined CS and HS algorithms. The utility of the proposed CSHS is demonstrated using four water distribution system design problems with increased scales and complexity. The obtained results reveal that the CSHS method outperforms the standard CS, as well as the majority of other meta-heuristics that have previously been applied to the case studies investigated, in terms of efficiently seeking optimal solutions. Furthermore, the CSHS has two control parameters that need to be fine-tuned compared to many other algorithms, which is appealing for its practical application as an extensive parameter-calibration process is typically computationally very demanding.

Opposition-Based Improved Harmony Search Algorithm Solve Unconstrained Optimization Problems

2013 ◽  
Vol 365-366 ◽  
pp. 170-173
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang ◽  
Li Qun Gao
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Solution Quality ◽  
Improved Harmony Search ◽  
Improved Harmony Search Algorithm ◽  
Harmony Memory

This paper develops an opposition-based improved harmony search algorithm (OIHS) for solving global continuous optimization problems. The proposed method is different from the classical harmony search (HS) in three aspects. Firstly, the candidate harmony is randomly chosen from the harmony memory or opposition harmony memory was generated by opposition-based learning, which enlarged the algorithm search space. Secondly, two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are adjusted dynamically with respect to the evolution of the search process. Numerical results demonstrate that the proposed algorithm performs much better than the existing HS variants in terms of the solution quality and the stability.

Opposition-Based Learning Harmony Search Algorithm Solving Unconstrained Optimization Problems

2014 ◽  
Vol 1006-1007 ◽  
pp. 1035-1038
Author(s):  
Ping Zhang ◽  
Peng Sun ◽  
Guo Jun Li
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Solution Quality ◽  
Opposition Based Learning ◽  
Unconstrained Optimization Problems ◽  
The Stability

Recently, a new meta-heuristic optimization algorithm–harmony search (HS) was developed,which imitates the behaviors of music improvisation. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to enhance diversity and prevent it trapped into local optimal. This paper develops an opposition-based learning harmony search algorithm (OLHS) for solving unconstrained optimization problems. The proposed method uses the best harmony to play pitch adjustment, and bring the concept of opposition-base learning into improvisation, which enlarged the algorithm search space. Besides, we design a new parameter setting strategy to directly tune the parameters in the search process, and balance the process of exploitation and exploration. Numerical results demonstrate that the proposed algorithm performs much better than the existing HS variants in terms of the solution quality and the stability.

scholarly journals Designation of Candidate Solutions in Differential Evolution Based on Bandit Algorithm and its Evaluation

2019 ◽  
Vol 23 (4) ◽  
pp. 758-766
Author(s):  
Masaya Sakakibara ◽  
◽  
Akira Notsu ◽  
Seiki Ubukata ◽  
Katsuhiro Honda
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimal Solution ◽  
Search Space ◽  
Optimization Method ◽  
Local Solution ◽  
Search Speed ◽  
Mathematical Programming Problems

We propose UCT-Grid Area Search (UCT-GAS), which is an efficient optimization method that roughly estimates specific values in areas, and consider exploration and exploitation in optimization problems. This approach divides the search space and imagines it to be a multi-armed bandit, which enables us to use bandit algorithms to solve mathematical programming problems. Although the search speed is fast than other search algorithm like differential evolution, it might converge to a local solution. In this study, we improve this algorithm by replacing its random search part with differential evolution after several searches. Comparative experiments confirmed the search ability of the optimal solution, and our method benefits by showing that it avoids falling into a local solution and that its search speed is fast.

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 Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

Optimization Method of the Multistage Axial Compressors Using CFD Simulation

2021 ◽  
pp. 2141006
Author(s):  
Grigorii Popov ◽  
Igor Egorov ◽  
Evgenii Goriachkin ◽  
Oleg Baturin ◽  
Daria Kolmakova ◽  
...  
Keyword(s):  
Cfd Simulation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Pressure Ratio ◽  
Optimization Method ◽  
Cfd Modeling ◽  
Turbine Engines ◽  
Stability Margins ◽  
Axial Compressors ◽  
Cfd Models

The current level of numerical methods of gas dynamics makes it possible to optimize compressors using 3D CFD models. However, the methods and means are not sufficiently developed for their wide application. This paper describes a new method for the optimization of multistage axial compressors based on 3D CFD modeling and summarizes the experience of its application. The developed method is a complex system of interconnected components (an effective mathematical model, a parameterizer, and an optimum search algorithm). The use of the method makes it possible to improve or provide the necessary values of the main gas-dynamic parameters of the compressor by changing the shape of the blades and their relative position. The method was tested in solving optimization problems for multistage axial compressors of gas turbine engines (the number of stages from 3 to 15). As a result, an increase in efficiency, pressure ratio, and stability margins was achieved. The presented work is a summary of a long-years investigation of the research team and aims at creating a complete picture of the obtained results for the reader. A brief description of the results of industrial compresses optimization contained in the paper is given as an illustration of the effectiveness of the developed methods.

Space Contraction and Partition Algorithm for Combinatorial Optimization

2011 ◽  
Vol 421 ◽  
pp. 559-563
Author(s):  
Yong Chao Gao ◽  
Li Mei Liu ◽  
Heng Qian ◽  
Ding Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Small Scale ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Intelligent Algorithms ◽  
Combinatorial Optimization Problems

The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary before or through the solving process in this algorithm, and solution information is used to estimate the landscape of search space, which enhances the speed of solving and solution quality. The algorithm breaks a new path for solving combinatorial optimization problems, and the results of experiments also testify its efficiency.

scholarly journals The Importance of Transfer Function in Solving Set-Union Knapsack Problem Based on Discrete Moth Search Algorithm

Mathematics ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 17 ◽  
Author(s):  
Yanhong Feng ◽  
Haizhong An ◽  
Xiangyun Gao
Keyword(s):  
Transfer Function ◽  
Knapsack Problem ◽  
Transfer Functions ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Continuous Optimization ◽  
Search Space ◽  
Discrete Search ◽  
Continuous Optimization Problems

Moth search (MS) algorithm, originally proposed to solve continuous optimization problems, is a novel bio-inspired metaheuristic algorithm. At present, there seems to be little concern about using MS to solve discrete optimization problems. One of the most common and efficient ways to discretize MS is to use a transfer function, which is in charge of mapping a continuous search space to a discrete search space. In this paper, twelve transfer functions divided into three families, S-shaped (named S1, S2, S3, and S4), V-shaped (named V1, V2, V3, and V4), and other shapes (named O1, O2, O3, and O4), are combined with MS, and then twelve discrete versions MS algorithms are proposed for solving set-union knapsack problem (SUKP). Three groups of fifteen SUKP instances are employed to evaluate the importance of these transfer functions. The results show that O4 is the best transfer function when combined with MS to solve SUKP. Meanwhile, the importance of the transfer function in terms of improving the quality of solutions and convergence rate is demonstrated as well.

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