Social Strategy of Particles in Optimization Problems

Borowska, Bożena

doi:10.1007/978-3-030-21803-4_54

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Then, the algorithm calculates

and

, according to the fitness function. The fitnesses of the particles are defined as an array

; the quality of the particles is assessed by means of an objective function of optimization problems [ 34 ]; and each element of the array is calculated using Equations ( 9 ), ( 19 ) and ( 24 ), respectively. These three equations represent the fitness function used in our method.…”

Section: Parallel Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Chen

Xiao

2021

Sensors

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An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

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“…Then, the algorithm calculates

and

, according to the fitness function. The fitnesses of the particles are defined as an array

Section: Parallel Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Chen

Xiao

2021

Sensors

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“…According to them, the social coefficient should be greater than the cognitive one, and they proved that c2 = 2.5 and c1 = 1.5 produce superior performance. Nonlinear, dynamically changing coefficients were proposed by Borowska [ 27 ]. In this approach, the values of coefficients were affected by the performance of PSO and the number of iterations.…”

Section: Introductionmentioning

confidence: 99%

Learning Competitive Swarm Optimization

Borowska¹

2022

Entropy

Self Cite

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Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

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“…Every optimization problem of these hierarchical nested systems has its decision-maker that tries to find his/her optimal solution [3,4]. Many applications appeared in the real-world in engineering design, traffic problems, problems, economic policy and so on are multi-level optimization problems [5][6][7][8][9]. Multi-level optimization problems consist of "N" optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…It produces difficulties in solving such as disconnectedness and non-convexity even to the simple problems. Such difficulties make BLPP is considered one of the strongly NP-hard problems [9]. BLPP solving techniques are divided into classical techniques and evolutionary techniques.…”

Section: Introductionmentioning

confidence: 99%

Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems

Abo-Elnaga

Nasr

2020

Symmetry

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Bi-level programming problem (BLPP) is an optimization problem consists of two interconnected hierarchical optimization problems. Solving BLPP is one of the hardest tasks facing the optimization community. This paper proposes a modified genetic algorithm and a chaotic search to solve BLPP. Firstly, the proposed algorithm solves the upper-level problem using a modified genetic algorithm. The genetic algorithm has modified with a new selection technique. The new selection technique helps the upper-level decision-maker to take an appropriate decision in anticipation of a lower level’s reaction. It distinguishes the proposed algorithm with a very small number of solving the lower-level problem, enhances the algorithm performance and fasts convergence to the solution. Secondly, a local search based on chaos theory has applied around the modified genetic algorithm solution. Chaotic local search enables the algorithm to escape from local solutions and increase convergence to the global solution. The proposed algorithm has evaluated on forty different test problems to show the proposed algorithm effectiveness. The results have analyzed to illustrate the new selection technique effect and the chaotic search effect on the algorithm performance. A comparison between the proposed algorithm results and other state-of-the-art algorithms results has introduced to show the proposed algorithm superiority.

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Social Strategy of Particles in Optimization Problems

Cited by 4 publications

References 27 publications

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Learning Competitive Swarm Optimization

Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems

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