Many Objective Particle Swarm Optimization

Figueiredo, Elliackin; Ludermir, Teresa B.; Bastos-Filho, Carmelo J. A.

doi:10.1016/j.ins.2016.09.026

Cited by 103 publications

(40 citation statements)

References 38 publications

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“…In addition, there are recently proposed MOPSO methods based on indicators, reference points, and balanceable fitness estimation, such as NMPSO [34], MaOPSO [39], and R2-MOPSO [40], which are used to solve highdimensional multiobjection optimization problems. NMPSO [34] uses a balanced fitness estimation method, which combines convergence and diversity distance to solve many-objective optimization problems.…”

Section: Some Current Multi/many-objective Particle Swarmmentioning

confidence: 99%

“…In order to enhance the performance of the algorithm, evolutionary search is used in external archives and a new PSO speed update equation is applied. e algorithm MaOPSO [39] adopts a set of reference points that are dynamically determined based on the search process and imposes the necessary selection pressure on the algorithm to make it converge to the true PF, while maintaining the diversity of the PF. R2-MOPSO [40] combines R2 performance metrics with particle swarm optimization and guides the search through a well-designed interactive process for solving many-objective optimization problems.…”

Section: Some Current Multi/many-objective Particle Swarmmentioning

confidence: 99%

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Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

Yang

Chen

Wang

et al. 2020

Computational Intelligence and Neuroscience

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The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.

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Section: Some Current Multi/many-objective Particle Swarmmentioning

confidence: 99%

Section: Some Current Multi/many-objective Particle Swarmmentioning

confidence: 99%

Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

Yang

Chen

Wang

et al. 2020

Computational Intelligence and Neuroscience

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“…In many-objective optimization problems where the number of objectives (often conflicting) exceeds three, the most important challenging issue is how to obtain a well distributed nondominated set of solutions which are close to the PF in the objective space. Different MaOPSO were proposed in several studies to resolve the lack of diversity and convergence in many-objective problems: In [25], the authors proposed a MaOPSO algorithm relying on a set of reference points to recognize the best solutions and guide the search process according to these reference points. The study in [26] proposed an algorithm that empowers the multiobjective structure of the PSO to deal with many-objective problems and suggest a R 2 indicator to guide the search.…”

Section: Many-objective Pso Algorithmmentioning

confidence: 99%

“…To study the impact of the new positions of the nomad nodes on the network performance and to evaluate the behaviors of the suggested algorithms (acMaPSO and acMaMaPSO), these latters are compared to the NSGA-III [39], MOEA/DD [40], Two_Arch2 [41], R 2 MPSO [26] and MaOPSO [25] which are recent many-objective optimization algorithms. Because of the stochastic nature of evolutionary algorithms and the necessity of a statistical test to compare two algorithms, the average values in the experimental scenario are obtained using 25 executions.…”

Section: Experimental/simulation Scenariomentioning

confidence: 99%

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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h i g h l i g h t s• Proving the efficiency of optimization algorithms in solving real-world problems.• A new concept of accent birds introduced to the particle swarm optimization. • A new hybrid scheme that integrates PSO and MAS.• The hybridization improves the performance of the original tested algorithms. • A comparison between simulation and experimental validation is given. a r t i c l e i n f o Keywords: Accent based PSO Multi-agent Many-objective optimization Experimental validation 3D indoor deployment DL-IoT collection networks a b s t r a c tThe 3D indoor deployment of sensor nodes is a complex real world problem, proven to be NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid approach relying on a novel bird's accent-based many objective particle swarm optimization algorithm (named acMaPSO) to resolve the problem of 3D indoor deployment on the Internet of Things collection networks. The new concept of bird's accent is presented to assess the search ability of particles in their local areas. To conserve the diversity of the population during searching, particles are separated into different accent groups by their regional habitation and are classified into different categories of birds/particles in each cluster according to their common manner of singing. A particle in an accent-group can select other particles as its neighbors from its group or from other groups (which sing differently) if the selected particles have the same expertise in singing or are less experienced compared to this particle. To allow the search escaping from local optima, the most expert particles (parents) ''die'' and are regularly replaced by a novice (newborn) randomly generated ones. Moreover, the hybridization of the proposed acMaPSO algorithm with multi-agent systems is suggested. The new variant (named acMaMaPSO) takes advantage of the distribution and interactivity of particle agents. Experimental, numerical and statistical found results show the effectiveness of the two proposed variants compared to different other recent state-of-the-art of many-objective evolutionary algorithms.✩ This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. * Correspondence to: bureau C107, Batiment C, 1 Place Georges positions and the topology of the network. In this study, we are interested in the deployment of nodes in a three-dimensional space that reflects the real topology of the RoI (region of interest) better than the two-dimensional case. We are particularly interested in improving the initial 3D indoor deployment by adding new nodes while optimizing a set of objectives such as coverage, connectivity, localization, quality of links and network utilization. These objectives will be detailed in the modeling section. To resolve the deployment problem in WSN, the topology of the network can be modeled as an identification problem in a graph as addressed in [1] and [2]. In this static case, the deployment algorithm is run off-...

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“…Generally speaking, Swarm intelligence optimization algorithms (SIOAs) are mostly inspired by the behaviors of biological swarm systems (e.g., bird flocking, foraging and courtship).There are several popular SIOAs, such as genetic algorithm (GA) [4], differential evolution algorithm (DE) [5], particle swarm optimization (PSO) [6,7], ant colony optimization (ACO) [8], artificial bee colony (ABC) [9,10], bat algorithm (BA) [11,12], bacteria foraging optimization algorithm (BFOA) [13], cuckoo search (CS) [14][15][16] and glowworm swarm optimization (GSO) [17,18], etc. In the past decades, these SIOAs have been widely applied to various optimization problems [19,20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems

Wang

Cui

2019

Algorithms

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In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention from scholars. Although many scholars have proposed improvement strategies for GSO and BFO to keep a good balance between convergence and diversity, there are still many problems to be solved carefully. In this paper, a new coupling algorithm based on GSO and BFO (MGSOBFO) is proposed for solving dynamic multi-objective optimization problems (dMOP). MGSOBFO is proposed to achieve a good balance between exploration and exploitation by dividing into two parts. Part I is in charge of exploitation by GSO and Part II is in charge of exploration by BFO. At the same time, the simulation binary crossover (SBX) and polynomial mutation are introduced into the MGSOBFO to enhance the convergence and diversity ability of the algorithm. In order to show the excellent performance of the algorithm, we experimentally compare MGSOBFO with three algorithms on the benchmark function. The results suggests that such a coupling algorithm has good performance and outperforms other algorithms which deal with dMOP.

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Many Objective Particle Swarm Optimization

Cited by 103 publications

References 38 publications

Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems

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