An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem

Gharehchopogh, Farhad Soleimanian; Barshandeh, Saeid; Javadi, Hatef

doi:10.1007/s00366-021-01470-z

Cited by 28 publications

(9 citation statements)

References 53 publications

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“…An improved binary SMA has been presented to resolve the 0–1 knapsack issue at various sizes [ 95 ]. Eight distinct transfer functions have been employed and analyzed in the binary SMA that is now being given.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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“…The VBR video content distribution solution is storage constrained, and the amount of video programs and video chunks that can be pushed is limited by the storage space of the gateway and the storage of the user terminals. Therefore, the push system of the content distribution scheme needs to maximize the value of the content distribution traffic as much as possible without exceeding the storage space of the gateway and the storage space of the user terminal, and this problem is a dynamic planning problem [8] .…”

Section: Programme Dynamic Planningmentioning

confidence: 99%

Efficient distribution solution for VBR encoded video

Wang,

Wang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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The popularity of the internet and the development of mobile internet has led to a rapid growth in internet users and smart devices and a rapid increase in internet traffic consumption[1]. Through an in-depth study of user video clicks and VBR encoded video[2] bit rates, we propose a new and effective push scheme. Based on the storage and channel configuration conditions in the hybrid network, as well as the VBR-encoded video characteristics, a push formula is given for video split, single programme and multiple programmes; with the goal of saving on-demand traffic and ensuring user storage prerequisites, the problem is dynamically planned for the rotation and push scheme selection problem for video split, single programme and multiple programmes. In this paper, the efficient content distribution scheme is based on the premise of guaranteeing memory storage, the goal of saving traffic, and the comparative reference quantities of push, rotation distribution and storage benefits. The dynamic planning problem is analyzed and optimized to a 0-1 backpack problem to find the push and rotation slice numbers and quantities. By improving the efficiency of Internet video content distribution and reducing distribution traffic, the efficient content distribution solution can effectively alleviate network congestion and the rapid growth of CDN investment, thus improving user experience.

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“…Researchers have suggested several evolutionary optimization strategies for single-objective and multi-objective optimization problems ( Steuer, 1986 ). These include the Adaptive neuro-fuzzy inference system-evolutionary algorithms hybrid models (ANFIS-EA) ( Roy et al, 2020 ), multi-objective optimization of grid-connected PV-wind hybrid system ( Barakat, Ibrahim & Elbaset, 2020 ), ant colony optimization (ACO) ( Dorigo, Birattari & Thomas, 2006 ), evolution strategy (ES) ( Mezura-Montes & Coello Coello, 2005 ), particle swarm optimization (PSO) ( Janga Reddy & Nagesh Kumar, 2021 ; Coello Coello, Pulido & Lechuga, 2004 ), genetic algorithm ( Deb et al, 2002 ), genetic programming (GP) ( Mugambi & Hunter, 2003 ), evolutionary programming (EP) ( Fonseca & Fleming, 1995 ), differential evolution (DE) ( Storn & Price, 1995 ), group counseling optimizer ( Eita & Fahmy, 2010 ; Ali & Khan, 2013 ), comprehensive parent selection-based genetic algorithm (CPSGA) ( Ali & Khan, 2012 ), whale optimization algorithm ( Masadeh, 2021 ), binary particle swarm optimization algorithm ( Sun et al, 2021 ), hybrid cat-particle swarm optimization algorithm ( Santoso et al, 2022 ), An enhanced binary slime mold algorithm ( Abdollahzadeh et al, 2021 ), improving flower pollination algorithm ( Basheer & Algamal, 2021 ), and 0/1 knapsack problem using genetic algorithm ( Singh, 2011 ), are some of the most common evolutionary optimization techniques. The group counseling optimizer (GCO) ensures uniqueness to prevent premature convergence.…”

Section: Introductionmentioning

confidence: 99%

An efficient optimizer for the 0/1 knapsack problem using group counseling

Ghadi

Alshloul²,

Nezami

et al. 2023

PeerJ Computer Science

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The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function. Optimization must reduce the given problem’s losses and disadvantages while maximizing its earnings and benefits. We all want optimal or, at the very least, suboptimal answers because we all want to live a better life. Group counseling optimizer (GCO) is an emerging evolutionary algorithm that simulates the human behavior of counseling within a group for solving problems. GCO has been successfully applied to single and multi-objective optimization problems. The 0/1 knapsack problem is also a combinatorial problem in which we can select an item entirely or drop it to fill a knapsack so that the total weight of selected items is less than or equal to the knapsack size and the value of all items is as significant as possible. Dynamic programming solves the 0/1 knapsack problem optimally, but the time complexity of dynamic programming is O(n3). In this article, we provide a feature analysis of GCO parameters and use it to solve the 0/1 knapsack problem (KP) using GCO. The results show that the GCO-based approach efficiently solves the 0/1 knapsack problem; therefore, it is a viable alternative to solving the 0/1 knapsack problem.

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An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem

Cited by 28 publications

References 53 publications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Efficient distribution solution for VBR encoded video

An efficient optimizer for the 0/1 knapsack problem using group counseling

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