scholarly journals Binary social group optimization algorithm for solving 0-1 knapsack problem

2022 ◽  
Vol 11 (1) ◽  
pp. 55-72 ◽  
Author(s):  
Anima Naik ◽  
Pradeep Kumar Chokkalingam
Keyword(s):  
Knapsack Problem ◽  
Social Group ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
High Dimensional ◽  
The Social ◽  
Social Group Optimization ◽  
Low Dimensional ◽  
Continuous Optimization Problems

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

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.

scholarly journals Text documents clustering using modified multi-verse optimizer

2020 ◽  
Vol 10 (6) ◽  
pp. 6361
Author(s):  
Ammar Kamal Abasi ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim ◽  
Mohammed A. Awadallah ◽  
...  
Keyword(s):  
Euclidean Distance ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Search Space ◽  
Discrete Optimization Problem ◽  
Text Documents ◽  
Text Document ◽  
Continuous Optimization Problems ◽  
Measure Entropy

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.

scholarly journals A comparative study of social group optimization with a few recent optimization algorithms

2020 ◽  
Author(s):  
Anima Naik ◽  
Suresh Chandra Satapathy
Keyword(s):  
Global Optimization ◽  
Social Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Search Optimization ◽  
The Social ◽  
Complex Optimization ◽  
Social Group Optimization ◽  
New Algorithms

Abstract From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

Adaptive Cellular Memetic Algorithms

2009 ◽  
Vol 17 (2) ◽  
pp. 231-256 ◽  
Author(s):  
Nguyen Quang Huy ◽  
Ong Yew Soon ◽  
Lim Meng Hiot ◽  
Natalio Krasnogor
Keyword(s):  
Memetic Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Memetic Algorithms ◽  
Adaptive Mechanisms ◽  
Cellular Genetic Algorithm ◽  
Local Solutions ◽  
Continuous Optimization Problems ◽  
Exploration Versus Exploitation

A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

scholarly journals A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1056 ◽  
Author(s):  
Feng ◽  
Yu ◽  
Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Orthogonal Design ◽  
Continuous Optimization ◽  
Monarch Butterfly ◽  
Migration Behavior ◽  
Monarch Butterflies ◽  
Monarch Butterfly Optimization ◽  
Low Dimensional ◽  
Continuous Optimization Problems

As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.

scholarly journals ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1456
Author(s):  
Stefka Fidanova ◽  
Krassimir Todorov Atanassov
Keyword(s):  
Decision Making ◽  
Knapsack Problem ◽  
Propositional Logic ◽  
Optimization Problems ◽  
Real Life ◽  
Combinatorial Optimization Problems ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Multiple Constraint

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

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