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About 10,000 papers for “Optimization algorithm”
Henrietta Dombrovskaya, Boris Novikov, Anna Bailliekova · Apress eBooks · 2024
The preceding chapters covered a lot of optimization techniques: not only different ways to optimize SQL statements but also how database design affects performance, the importance of working together with application developers, the use of functions, and many other aspects of database performance.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
With the increasing complexity of modern engineering optimization problems, traditional optimization algorithms often get stuck in local optima when dealing with high-dimensional, multimodal, and non-convex problems. Animal behavior in nature provides rich inspiration for optimization algorithms, especially the marsupial anteater (Tamandua), whose flexible predation behavior and strong adaptability to the environment offer new ideas for continuous optimization problems. This paper proposes an optimization algorithm based on the behavior of the marsupial anteater, named the Tamandua Optimization Algorithm (TOA). Inspired by the marsupial anteater's predation strategy, global migration behavior, and energy allocation mechanism, this algorithm achieves efficient optimization search through a combination of multi-scale olfactory perception, local fine-grained search and global exploration, historical trajectory memory, and group collaborative information flow. This paper provides a detailed mathematical description of the algorithm modeling, iterative update mechanism, search strategy, and energy adjustment, providing new theoretical methods and application foundations for optimization problems.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
With the rapid development of computational intelligence technology, various swarm intelligence optimization algorithms have shown good performance in engineering optimization, combinatorial optimization, and solving complex systems. However, existing optimization algorithms still suffer from slow convergence speed, susceptibility to local optima, and insufficient global search capability when facing high-dimensional, multi-modal, or nonlinear complex problems. To address this, this paper proposes a novel swarm intelligence optimization algorithm based on the behavioral characteristics of cockatiels—the Cockatiel Optimization Algorithm (COA). This algorithm draws on the cockatiel's flocking flight behavior, vocal communication, and environmental adaptation mechanisms, achieving efficient search of the solution space through individual experience attraction, global optimum guidance, and neighbor information sharing. The paper details the algorithm's mathematical model, position update strategy, group information transmission mechanism, and escape strategy, and verifies its superior performance on standard test functions through a series of numerical experiments. Experimental results show that COA outperforms traditional swarm intelligence algorithms in global search, convergence speed, and multi-modal function optimization capability.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms, by simulating the collaborative behavior of biological groups in nature, exhibit strong global search capabilities in complex optimization problems. However, many existing algorithms are designed based on the assumptions that "the optimal solution is reliable" and "failed searches are invalid perturbations," thus ignoring the informational value inherent in perceptual uncertainty and erroneous decisions. To address this, this paper proposes a novel swarm intelligence optimization method—the Sparrowhawk Optimization Algorithm. This algorithm uses the hunting behavior of a sparrowhawk as a prototype, modeling the search process from aspects such as visual perception, dive path, and misjudgment correction. By introducing a visual credibility field, continuous dive path modeling, a misjudgment reverse gain mechanism, and a cognitive energy budget model, the Sparrowhawk Optimization Algorithm breaks through the traditional paradigms of "one-step update" and "absolutely reliable optimal solution," making the search process more dynamically adaptable and interpretable. This algorithm provides a new approach for constructing swarm intelligence optimization models with cognitive characteristics.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm optimization algorithms have been widely studied in continuous optimization, combinatorial optimization, and engineering optimization due to their simple structure, strong robustness, and good adaptability to complex nonlinear problems. However, most existing swarm optimization algorithms still rely on a single optimal individual as a global guide, which easily leads to premature convergence and search stagnation in multi-peak or complex search spaces. To address these shortcomings, this paper proposes a novel swarm optimization method—the Jackal Optimization Algorithm. Inspired by the opportunistic predation behavior of jackals in nature, this algorithm constructs a search mechanism from multiple levels, including group decision-making, opportunity perception, memory evolution, and changes in group structure. The algorithm guides the individual search direction through an opportunity potential field, introduces a false prey mechanism to enhance search diversity, and combines a memory decay containment strategy with a group fission-reorganization mechanism to improve local development performance while maintaining global exploration capabilities. Theoretically, this method differs from existing wolf pack, whale pod, and particle swarm optimization algorithms, providing a new modeling paradigm for swarm optimization.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms, by simulating the collaborative behavior of biological groups in nature, exhibit strong global search capabilities in complex optimization problems. However, many existing algorithms are designed based on the assumptions that "the optimal solution is reliable" and "failed searches are invalid perturbations," thus ignoring the informational value inherent in perceptual uncertainty and erroneous decisions. To address this, this paper proposes a novel swarm intelligence optimization method—the Sparrowhawk Optimization Algorithm. This algorithm uses the hunting behavior of a sparrowhawk as a prototype, modeling the search process from aspects such as visual perception, dive path, and misjudgment correction. By introducing a visual credibility field, continuous dive path modeling, a misjudgment reverse gain mechanism, and a cognitive energy budget model, the Sparrowhawk Optimization Algorithm breaks through the traditional paradigms of "one-step update" and "absolutely reliable optimal solution," making the search process more dynamically adaptable and interpretable. This algorithm provides a new approach for constructing swarm intelligence optimization models with cognitive characteristics.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
To address the problems of premature convergence, over-reliance on instantaneous optimal solutions, and static population structure inherent in existing swarm intelligence optimization algorithms for complex, multimodal, and non-stationary optimization problems, this paper proposes a novel swarm intelligence optimization method—the Grouse Optimization Algorithm (GOA). Inspired by the grouse's ground foraging, sudden take-off, population splitting and regrouping, and risk-avoidance behaviors exhibited in high-pressure ecosystems, GOA systematically reconstructs the traditional optimization paradigm from three levels: search target modeling, population structure organization, and individual behavioral decision-making. GOA no longer treats the global optimum as the sole attraction point but introduces a habitat center modeling method based on trajectory stability. It achieves dynamic adaptive control of the search process through an evolutionary trajectory credibility mechanism, a split-regrouping search structure, an anti-habitat disturbance strategy, and a behavioral state evolution graph. Theoretically, this algorithm constructs a stochastic behavioral evolution optimization framework oriented towards long-term adaptability, providing a new research approach for the design of swarm intelligence optimization algorithms.
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Parsa Parsafar, Iman Esmaeili Afrokati · Pattern Analysis and Applications · 2026
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Jiangli Yu (4601986), Gaoyi Liang (22311429), Lei Wang (6656) et al. · Figshare · 2025
<div> This study focuses on the short-term power prediction of photovoltaic power stations, aiming to address the intermittent and fluctuating problems of photovoltaic power generation, in order to improve the prediction accuracy and ensure the stable operation of the power system. Innovatively introduce the Kepler algorithm into this field, deeply analyze historical data, and mine the nonlinear relationships among various factors to lay a solid data foundation for subsequent predictions. The VMD-CNN-LSTM combined model is constructed. It is a model combining variational mode decomposition (VMD), convolutional neural network (CNN) and long short term memory network (LSTM), VMD adaptively decomposes the original power sequence based on frequency characteristics to reduce data complexity. CNN accurately extracts spatial features from the decomposed modal components; LSTM leverages its expertise in processing time series data to capture the dynamic trends of power changes, and the three work in synergy. Meanwhile, the Kepler optimization algorithm (KOA) is deeply integrated with this model to optimize the entire process of the model from data preprocessing to result correction. Verified by examples, compared with the traditional prediction model, the proposed method has significant optimization in evaluation indicators such as root mean square error and mean absolute error, which strongly proves its effectiveness and superiority. It provides an innovative idea and reliable method for the short-term power prediction of photovoltaic power stations and is of great significance for promoting the grid connection of photovoltaic power generation and the optimization of the power system. </div>
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André X. C. N. Valente (17126), Joseph Zischkau (301387), Joo Heon Shin (301388) et al. · Figshare · 2015
<p>The algorithm was applied to homogenize the Hamza et al. cohort. The algorithm parameters were selected based on the value of the potential p-value bias test (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048653#pone-0048653-t001" target="_blank">Table 1</a> and main text). Graph <b>a</b>) shows potential p-value bias versus % of cohort weight decrease. A 7% cohort weight decrease was selected, as weight decreases beyond this value produced only a marginal further decline in the potential p-value bias. <b>b</b>) The sphere weight parameter was set to 30. <b>c</b>) A 0.2 knocked-down weight was selected. In each of these graphs, the two parameters not represented are held at their selected values.</p>
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Emad Qasim · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Brenda Servaz Tubaña, In Corn (zea Mays L · 2014
ii ACKNOWLEDGEMENTS I thank our Lord Jesus Christ for giving me the ability to complete this degree. Your love, faithfulness and guidance have been my instruments for this achievement. I would like to thank my husband, Edwin, and my children, Elaisa and Casiano III, for their love, support and understanding while pursuing this degree. I am also thankful to Raun family, and to my relatives, brothers and sisters in Christ and friends for being supportive and helpful in anyway they can. I am also grateful to the Department of Plant and Soil Sciences for granting me the opportunity to
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E. Harth, T.E. Kalogeropoulos, Wanmin Liu et al. · 1990
Abstract : This report provides results of the investigation and application of the Alopex Algorithm to optimization problems. The Alopex Algorithm is a stochastic multiparameter optimization procedure. This algorithm addresses the two optimization problems of resource allocation and pattern recognition. The assigned tasks consist of two parts: (1) Recognition of 3-D objects given a 2-D projection. (2) Optimal resource allocation. Both assignments involved application of the optimization algorithm Alopex which was developed by the principal investigators. We investigated 3-D objects with a view toward identification of airplanes. A system capable of rapid identification of airplanes can be built based on our findings. For the second assignment we investigated the problem of assigning in an optimal way a number of guns to targets. Alopex has been found to converge rapidly to optimal assignments. We also compared the performance of the Alopex algorithm with the widely used method of simulated annealing. Alopex was found to converge considerably faster in all examples tried.
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A. Kaveh · 2021
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2025
This paper proposes a novel swarm intelligence optimization algorithm, the Bison Optimization Algorithm (BOA), inspired by the herd migration, resource perception, and individual competitive behavior of American bison. By incorporating resource-aware local search, a conflict-competition perturbation mechanism, and a dynamic energy-sharing model, the algorithm seamlessly integrates local refined search with global leap exploration. By mimicking the natural behavior of bison, this approach imbues the swarm with adaptive diversity, the ability to escape local optima, and collaborative optimization within the search space. This paper describes the algorithm modeling, mathematical formulation, and workflow in detail, providing a theoretical foundation for further research in optimization algorithms.
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Hla Moe. · DR-NTU (Nanyang Technological University) · 2008
Vertical transportation is essential for high rise buildings. An efficient lift system should have minimum waiting time at any floor level, rapid transportation time to the desired destination, passenger comfort in the lift and lesser travelling distance to reduce the energy and extend equipment life. There have been a number of control algorithms developed to improve the quality of the lift system. There are many factors involved in the analysis of the lift control system. This report describes the various required parameters affecting the performance of a lift control system and proposes a control algorithm for lift group control system. Our algorithm is developed in a general setting to optimize the efficiency of lifts in buildings such as reduced waiting times for lift passengers on each landing and other associated factors. A software package called CALCSS (Computer Aided Lift Control System Simulation), written in Visual Basic language, has been developed to compare the conventional method with our optimal method in lift efficiency optimization.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
With the rapid development of computational intelligence technology, various swarm intelligence optimization algorithms have shown good performance in engineering optimization, combinatorial optimization, and solving complex systems. However, existing optimization algorithms still suffer from slow convergence speed, susceptibility to local optima, and insufficient global search capability when facing high-dimensional, multi-modal, or nonlinear complex problems. To address this, this paper proposes a novel swarm intelligence optimization algorithm based on the behavioral characteristics of cockatiels—the Cockatiel Optimization Algorithm (COA). This algorithm draws on the cockatiel's flocking flight behavior, vocal communication, and environmental adaptation mechanisms, achieving efficient search of the solution space through individual experience attraction, global optimum guidance, and neighbor information sharing. The paper details the algorithm's mathematical model, position update strategy, group information transmission mechanism, and escape strategy, and verifies its superior performance on standard test functions through a series of numerical experiments. Experimental results show that COA outperforms traditional swarm intelligence algorithms in global search, convergence speed, and multi-modal function optimization capability.
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms are a class of methods that solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a novel swarm intelligence optimization algorithm—the Hillstar Optimization Algorithm (HSOA), inspired by the foraging, singing, and social behaviors of myna birds in their natural environment. HSOA achieves a balance between global search and local exploitation by simulating the myna bird's multiple behavioral strategies, group cooperation mechanisms, and experience memory mechanisms. The algorithm introduces random foraging behavior, local concentrated search behavior, defensive escape behavior, contrastive learning mechanisms, and experience base memory mechanisms, which can effectively improve the convergence speed and global search capability of the algorithm in high-dimensional complex optimization problems. This paper theoretically analyzes the convergence, swarm intelligence, and adaptive cooperation capabilities of the algorithm, and provides detailed mathematical formulas describing the algorithm's update rules, providing a theoretical foundation for subsequent experiments and applications.
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Zhang, Jincheng · Zenodo (CERN European Organization for Nuclear Research) · 2026
Swarm intelligence optimization algorithms, as a global optimization method based on the simulation of biological swarm behavior, have wide applications in complex function optimization, path planning, combinatorial optimization, and engineering optimization. This paper proposes a novel swarm intelligence optimization algorithm—the Mouse Optimization Algorithm (MOA), inspired by the behavioral characteristics of mice searching for food in their natural environment, including olfactory search, exploration-utilization balance, swarm information sharing, and dynamic behavioral strategies. MOA abstracts the search behavior of a mouse swarm into a solution method for a continuous optimization problem through mathematical modeling and plain text formulas. The algorithm design incorporates multiple modules such as local search, global exploration, multi-neighbor information sharing, random jumping, and fitness pressure mechanisms, achieving an organic combination of intelligence, global search capability, and adaptive search strategies. This paper provides a detailed analysis of the core mechanisms of the MOA algorithm and describes its update rules and dynamic strategies using mathematical formulas, providing a basic framework for further theoretical research and engineering applications.
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Jincheng Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026
To address the multi-peak structure, local extremum traps, and high-dimensional coupling characteristics inherent in complex non-convex optimization problems, this paper constructs a scorpionfly optimization algorithm model with an energy memory mechanism and topological adaptive structure. Building upon the existing curvature-sensing and information entropy adjustment framework, a non-equilibrium energy system, an adaptive topological phase transition mechanism, and an anisotropic attack tensor are further introduced to establish a unified discrete and continuous-time dynamical system expression. By introducing individual energy variables to characterize stagnation and burst behaviors, a population structure phase transition model is constructed, and dimensionality-level enhancement is achieved by constructing an attack tensor using dimensional variance. The stability, complexity, and dynamic characteristics of the system are analyzed. The results show that the model effectively enhances adaptability to complex search spaces while maintaining linear complexity.
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