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.

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.

Optimization Method Based on a Mix Strategy

2013 ◽  
Vol 816-817 ◽  
pp. 1154-1157
Author(s):  
Xu Yin ◽  
Ai Min Ji
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Solution ◽  
Collaborative Optimization ◽  
Design Variables ◽  
Gradient Optimization ◽  
Local Optimal Solution ◽  
Optimal Values ◽  
Low Efficiency

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

scholarly journals SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems.  This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.  

scholarly journals Genetic Algorithm – A Sensible Evolutionary Optimization Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 2805-2809
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Effective Population ◽  
Natural Systems ◽  
Heuristic Search Algorithm ◽  
Feasible Solutions

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

A plague epidemic disease optimizing approach

2021 ◽  
pp. 1-19
Author(s):  
Ya Zhou ◽  
Jinding Gao
Keyword(s):  
Infectious Disease ◽  
Optimization Problems ◽  
Random Search ◽  
Optimal Solution ◽  
Epidemic Disease ◽  
Physical Strength ◽  
Pulse Vaccination ◽  
Effective Contact ◽  
Study Results ◽  
Search Speed

In order to solve some optimization problems with multi-local optimal solutions, a plague infectious disease optimization (PIDO) algorithm is proposed by the dynamic model of plague infectious disease with pulse vaccination and time delay. In this algorithm, it is assumed that there are several villagers living in a village, each villager is characterized by some characteristics. The plague virus is prevalent in the village, and the villagers contract the infectious disease through effective contact with sick rats. The plague virus attacks is the few characteristics of the human body. Under the action of the plague virus, the growth status of each villager will be randomly transformed among 4 states of susceptibility, exposure, morbidity and recovery, thus a random search is achieved for the global optimal solution. The physical strength degree of villagers is described by the human health index (HHI). The higher the villager’s HHI index, the stronger the physique and the higher the surviving likelihood. 9 operators (S_S, S_E, E_E, E_I, E_R, I_I, I_R, R_R, R_S) are designed in the PIDO algorithm, and each operator only deals with the 1/1000∼1/100 of the total number of variables each time. The case study results show that PIDO algorithm has the characteristics of fast search speed and global convergence, and it is suitable for solving global optimization problems with higher dimensions.

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.

scholarly journals Optimum design of 3R robot manipulador by using Improved Differential Evolution implemented in parallel computation

2020 ◽  
Vol 1 (2) ◽  
pp. 83-103
Author(s):  
Sezimária De Fátima Pereira Saramago ◽  
José Laércio Doricio ◽  
Milena Almeida Leite Brandão
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Robot Manipulator ◽  
Optimal Solution ◽  
Optimization Method ◽  
Objective Functions ◽  
Basic Algorithm ◽  
Promising Alternative ◽  
Multi Objective ◽  
De Algorithm

In recent decades the great interest in Evolutionary Algorithms (EAs) has boosted their development leading to a significant improvement in their efficiency and applicability. Thus, EAs have been applied to solve optimization problems in different areas of knowledge. A promising optimization method known as Differential Evolution (DE), which belongs to the class of AEs, has attracted the attention of researchers. The DE algorithm is simple, robust and efficient. However, by testing with classical optimization problems noticed that sometimes the results obtained with DE are not as satisfactory as expected or that in many cases the algorithm ends the search for the optimal solution prematurely. Recently, with the advancement and greater availability of computer technology, the scientific community has been thinking about the implementation of optimization algorithms in parallel in order to reduce the processing time. The main objective of this paper is to present an improvement of the Differential Evolution optimization method, proposing modifications to the basic algorithm by using shuffled complex and making it able to work with parallel computing. The proposed methodology is applied to the optimal design of an orthogonal 3R robot manipulator that takes into account the characteristics of its workspace. For this purpose, a multi-objective optimization problem is formulated to obtain the optimal geometric parameters for the robot. The maximum workspace volume, the maximum system stiffness and the optimum dexterity are considered as the multi-objective functions. The results show that the procedure represents a promising alternative for the type of problem presented above.

scholarly journals Solving integer programming problems by using population-based beetle antennae search algorithm

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems. This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.

Stress-based binary differential evolution for topology optimization of structures

2010 ◽  
Vol 224 (2) ◽  
pp. 443-457 ◽  
Author(s):  
C-Y Wu ◽  
K-Y Tseng
Keyword(s):  
Topology Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Method ◽  
Optimal Topology ◽  
Combinatorial Optimization Problems ◽  
Point Representation ◽  
Mutation Mechanism ◽  
Binary Differential Evolution

Differential evolution (DE) is a heuristic optimization method used to solve many optimization problems in real-value search space. It has the advantage of incorporating a relatively simple and efficient form of mutation and crossover. However, the operator of DE is based on floating-point representation only, and is difficult to use in solving combinatorial optimization problems. In this article, a modified binary DE is developed using binary bit-string frameworks with a logical operation binary mutation mechanism. Further, a new stress-based binary mutation mechanism is also proposed to drive the binary DE search towards the optimal topology of the structure with higher performance and fewer objective function evaluations. The numerical results show that the performance of the proposed algorithm using stress-based binary mutation has high capability and efficiency in topology optimization of the structure.

Optimization Method RasID-GA for Numerical Constrained Optimization Problems

2007 ◽  
Vol 11 (5) ◽  
pp. 469-477 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Random Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Optimization Method ◽  
Penalty Functions ◽  
Objective Functions ◽  
Constrained Optimization Problems

This paper proposes Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) for constrained optimization. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for unconstrained optimization problems. In general, it is very difficult to find an optimal solution for constrained optimization problems because their feasible solution space is very limited and they should consider the objective functions and constraint conditions. The conventional constrained optimization methods usually use penalty functions to solve given problems. But, it is generally recognized that the penalty function is hard to handle in terms of the balance between penalty functions and objective functions. In this paper, we propose a constrained optimization method using RasID-GA, which solves given problems without using penalty functions. The proposed method is tested and compared with Evolution Strategy with Stochastic Ranking using well-known 11 benchmark problems with constraints. From the Simulation results, RasID-GA can find an optimal solution or approximate solutions without using penalty functions.

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