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A Collective Intelligence Strategy for Enhancing population-based Optimization algorithms

A Collective Intelligence Strategy for Enhancing Population-...

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Bidgoli, Azam Asilian Rahnamayan, Shahryar Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (纸本)9781728169293

population-based algorithms are a well-established category of metaheuristic optimization algorithms in which individuals collaborate with each other to find the optimal solution in a search space. During the search process, each individual provides a partial intelligence which can assist the population movement toward promising regions. In this paper, a dimension-wise strategy is proposed to collect the intelligence of whole population to generate a new trial candidate solution. For new individual, the value of each variable is calculated using the votes of a more-crowded cluster of individuals obtained on each dimension (one-dimensional clustering). Accordingly, a group of candidate solutions in the population collaborate to determine a variable value of new individual. Utilizing this strategy, collective intelligence (CI) aims the algorithm to find better candidate solutions. Since the proposed method keeps untouched all other parts of the algorithm, it can be used with any population-based algorithm. This paper presents the modification of two well-known population-based algorithms based on the proposed strategy in utilizing Collective Intelligence (CI), Differential Evolution (CIDE) and Particle Swarm Optimization (CIPSO). In conducted experiments, two proposed algorithms are compared with classical version of DE and PSO on 30 functions of CEC-2017 benchmark. The results indicate that the proposed method generates an individual with better objective function value than many of the individuals in the population which leads totally better results in overall.

关键词： Collective Intelligence population-based algorithms Differential Evolution (DE) Particle Swarm Optimization (PSO) Metaheuristics Voting Dimension-wise Clustering

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Multi-Objective population-based Optimization algorithms for Scheduling of Manufacturing Entities 26

Multi-Objective Population-based Optimization Algorithms for...

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26th International Conference on Methods and Models in Automation and Robotics

作者： Petrovic, Milica Jokic, Aleksandar Miljkovic, Zoran Kulesza, Zbigniew Univ Belgrade Dept Prod Engn Fac Mech Engn Belgrade Serbia Bialystok Tech Univ Fac Elect Engn Dept Automat Control & Robot Bialystok Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm.

关键词： Multi-objective optimization population-based algorithms Manufacturing resources scheduling

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Integration of group method of data handling (GMDH) algorithm and population-based metaheuristic algorithms for spatial prediction of potential groundwater

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ENVIRONMENTAL EARTH SCIENCES 2022年第20期81卷 485-485页

作者： Amiri-Doumari, Sahar Karimipour, Ahmadreza Nayebpour, Seyed Nader Hatamiafkoueieh, Javad Ardakan Univ Fac Agr & Nat Resources Dept Nat Engn Ardakan Iran Payame Noor Univ Dept Civil Engn Fac Engn Shahrekord Iran Islamic Azad Univ South Tehran Branch Dept Petr & Min Engn Tehran Iran Peoples Friendship Univ Russia Acad Engn Dept Mech & Control Proc RUDN Univ Moscow 117198 Russia

Potable water scarcity is a worldwide issue that can be addressed by looking for new regions with adequate groundwater. The study used a combination of three metaheuristic optimization algorithms (particle swarm optimization (PSO), grey wolf optimizer (GWO), and ant colony optimization (ACO)) and a group method of data handling (GMDH) algorithm to delineate groundwater potential zones in Boroujen, Iran. The spatial datasets were created using twelve conditioning factors and spring data (429 locations). The mentioned dataset was divided into two training (70%) and validation (30%) groups. The weights generated from the frequency ratio (FR) method were utilized as modeling inputs. The groundwater potential maps were created using the GMDH, GMDH-PSO, GMDH-GWO, and GMDH-ACO algorithms. With an area under the receiver operating characteristic curve (AUC-ROC) of 87.4%, the GMDH-PSO algorithm outperformed the GMDH-GWO, GMDH-ACO, and GMDH algorithms, which had AUC-ROC values of 85.1%, 83.3% and 80.3%, respectively. Furthermore, the regression relief (Rrelief) method revealed that rainfall, land use/cover, and altitude play a greater role in groundwater assessment. The findings of this research could help with groundwater resource management and groundwater investment planning for long-term sustainability.

关键词： Groundwater population-based algorithms Machine learning Spatial modelling Geographic information system (GIS)

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population-level center-based sampling for meta-heuristic algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ibrahim, Amin Bidgoli, Azam Asilian Makrehchi, Masoud Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON Canada Brock Univ Dept Engn St Catharines ON Canada Ontario Tech Univ Fac Business & Informat Technol Oshawa ON Canada Wilfrid Laurier Univ Fac Sci Waterloo ON Canada

In recent years, the challenge of enhancing the efficiency and effectiveness of meta-heuristic algorithms has gained significant attention. Center-based sampling has shown promise in addressing this challenge, yet its application often requires customization for specific algorithms, limiting its generalizability. This study identifies a gap in the literature regarding the operation-independent application of center-based sampling. To address this, we propose a novel center-based sampling strategy at the population level, which can be seamlessly integrated into any population-based optimization algorithm. Our approach employs a collaborative multi-parent method to generate multiple center-based solutions, thereby increasing diversity and exploiting the solution space more effectively. We introduce two specific strategies: cluster-driven center-based sampling for single-objective optimization and ranking-driven center-based sampling for multi-objective optimization. The performance of these strategies is evaluated using the benchmark functions for the CEC-2017 competition on 5 single- and 6 many-objective evolutionary algorithms, demonstrating 40% similar to 100% statistical fitness improvement ratio over parent meta-heuristic algorithms, respectively. These findings highlight the potential of population-level center-based sampling to enhance the performance of meta-heuristic algorithms.

关键词： Center-based sampling population-based algorithms Single-objective optimization Multi-objective optimization Meta-heuristic algorithms

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Leveraging social networks as an optimization approach

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2025年 26卷

作者： Ghadirian, Hamed Mousavirad, Seyed Jalaleddin Univ Kashan Dept Elect & Comp Engn Kashan Iran Mid Sweden Univ Dept Comp & Elect Engn Sundsvall Sweden

Metaheuristic algorithms have become powerful tools for solving complex optimization problems. Consensus-based optimization (CBO), inspired by social interactions, models a network where agents adjust their positions by learning from their neighbors. While effective, CBO relies on a fixed network structure, limiting its adaptability. To overcome this, we propose the Human Generation (HG) algorithm, which extends CBO by incorporating a two-layer influence mechanism. The first layer mimics kinship-based learning, ensuring local refinement, while the second layer models elite-following behavior, enabling efficient global exploration. This structured adaptation enhances both convergence speed and solution accuracy. We evaluate HG across unimodal, multimodal, and complex optimization problems, as well as a real-world image thresholding application. Experimental results demonstrate that HG consistently outperforms CBO and other state-of-the-art algorithms, making it a robust optimization approach.

关键词： Evolutionary algorithm Social network Optimization Consensus-based optimization population-based algorithms Image thresholding

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population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 14卷 31-47页

作者： Helbig, Marde Engelbrecht, Andries R. Meraka Inst CSIR Scientia ZA-0184 Pretoria South Africa Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisation (MOO) have focussed on static MOO (SMOO) and dynamic single-objective optimisation. However, in recent years, algorithms were proposed to solve dynamic MOO (DMOO). This paper provides an overview of the algorithms that were proposed in the literature to solve DMOOPs. In addition, challenges, practical aspects and possible future research directions of DMOO are discussed. (C) 2013 Elsevier B.V. All rights reserved.

关键词： population-based algorithms Dynamic multi-objective optimization Dynamic multi-objective optimization algorithms

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population-based Algorithm Portfolios for Numerical Optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2010年第5期14卷 782-800页

作者： Peng, Fei Tang, Ke Chen, Guoliang Yao, Xin Univ Sci & Technol China Nat Inspired Computat & Applicat Lab Sch Comp Sci & Technol Hefei 230027 Peoples R China Natl High Performance Comp Ctr Hefei 230027 Peoples R China Univ Birmingham Sch Comp Sci Ctr Excellence Res Computat Intelligence & Applic Birmingham B15 2TT W Midlands England

In this paper, we consider the scenario that a population-based algorithm is applied to a numerical optimization problem and a solution needs to be presented within a given time budget. Although a wide range of population-based algorithms, such as evolutionary algorithms, particle swarm optimizers, and differential evolution, have been developed and studied under this scenario, the performance of an algorithm may vary significantly from problem to problem. This implies that there is an inherent risk associated with the selection of algorithms. We propose that, instead of choosing an existing algorithm and investing the entire time budget in it, it would be less risky to distribute the time among multiple different algorithms. A new approach named population-based algorithm portfolio (PAP), which takes multiple algorithms as its constituent algorithms, is proposed based upon this idea. PAP runs each constituent algorithm with a part of the given time budget and encourages interaction among the constituent algorithms with a migration scheme. As a general framework rather than a specific algorithm, PAP is easy to implement and can accommodate any existing population-based search algorithms. In addition, a metric is also proposed to compare the risks of any two algorithms on a problem set. We have comprehensively evaluated PAP via investigating 11 instantiations of it on 27 benchmark functions. Empirical results have shown that PAP outperforms its constituent algorithms in terms of solution quality, risk, and probability of finding the global optimum. Further analyses have revealed that the advantages of PAP are mostly credited to the synergy between constituent algorithms, which should complement each other either over a set of problems, or during different stages of an optimization process.

关键词： Algorithm portfolios global optimization metaheuristic algorithms numerical optimization population-based algorithms

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A population-based approach to the resource-constrained project scheduling problem

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ANNALS OF OPERATIONS RESEARCH 2004年第1-4期131卷 305-324页

作者： Valls, V Ballestín, F Quintanilla, S Univ Valencia Fac Matemat Dpto Estadist & Invest Operat E-46100 Valencia Spain Univ Valencia Fac Econ & Empresariales Dpto Econ Financiera & Matemat Valencia Spain

We present a population-based approach to the RCPSP. The procedure has two phases. The first phase handles the initial construction of a population of schedules and these are then evolved until high quality solutions are obtained. The evolution of the population is driven by the alternative application of an efficient improving procedure for locally improving the use of resources, and a mechanism for combining schedules that blends scatter search and path relinking characteristics. The objective of the second phase is to explore in depth those vicinities near the high quality schedules. Computational experiments on the standard j120 set, generated using ProGen, show that our algorithm produces higher quality solutions than state-of-the-art heuristics for the RCPSP in an average time of less than five seconds.

关键词： population-based algorithms scatter search path relinking hybrid heuristics resource constrained project scheduling

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population-based Algorithm with Selectable Evolutionary Operators for Nonlinear Modeling 38th

Population-Based Algorithm with Selectable Evolutionary Oper...

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38th International Conference on Information Systems Architecture and Technology (ISAT)

作者： Lapa, Krystian Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland

ISBN: (纸本)9783319672205;9783319672199

In this paper a new population-based algorithm for nonlinear modeling is proposed. Its advantage is the automatic selection of evolutionary operators and their parameters for individuals in population. In this approach evolutionary operators are selected from a large set of operators, however only the solutions that use low number of operators are promoted in population. Moreover, assigned operators can be changed during evolution of population. Such approach: (a) eliminates the need for determining detailed mechanism of the population-based algorithm, and (b) reduces the complexity of the algorithm. For the simulations typical nonlinear modeling benchmarks are used.

关键词： population-based algorithms Fuzzy systems Nonlinear modeling Selection of evolutionary operators

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Negative Space-based population Initialization Algorithm (NSPIA) 17th

Negative Space-Based Population Initialization Algorithm (NS...

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17th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Lapa, Krystian Cpalka, Krzysztof Przybyl, Andrzej Grzanek, Konrad Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland Univ Social Sci Informat Technol Inst PL-90113 Lodz Poland Clark Univ Worcester MA 01610 USA

ISBN: (纸本)9783319912530;9783319912523

There are many different varieties of population-based algorithms. They are interesting techniques for investigating of the search space of solutions and can be used, among others, to solve optimization problems. They usually start from initialization of a population of individuals, each of which encodes parameters of a single solution to the problem under consideration. After initialization, the preselected individuals are processed in a way that depends on the specifics of the algorithm. Therefore, properly implemented population initialization can significantly improve the algorithm's operation and increase the quality of obtained results. This article describes a new population initialization algorithm. Its characteristic feature is the marginalization of those areas of the search space, in which once localized individuals were assessed as not satisfying. The proposed algorithm is of particular importance for problems in which no information is available that can improve the search procedure (black-box optimization). To test the proposed algorithm simulations were carried out using well-known benchmark functions.

关键词： population-based algorithms population initialization Black-box optimization Negative space

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