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A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems
Published 2021“…In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. …”
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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems
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Optimal VAR Dispatch Using a Multiobjective Evolutionary Algorithm
Published 2005“…A new Strength Pareto Evolutionary Algorithm based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and non-commensurable objectives. …”
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A Parallel Tabu Search Algorithm for Optimizing Multiobjective VLSI Placement
Published 2005“…In this paper, we present a parallel tabu search (TS) algorithm for efficient optimization of a constrained multiobjective VLSI standard cell placement problem. …”
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Multiobjective optimal power flow using strength Pareto evolutionary algorithm
Published 2004“…A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and non-commensurable objectives. …”
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A novel multiobjective evolutionary algorithm for optimal reactive power dispatch problem
Published 2003“…A new Strength Pareto Evolutionary Algorithm (SPEA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. …”
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Trial-based dominance for comparing both the speed and accuracy of stochastic optimizers with standard non-parametric tests
Published 2023“…Simulations demonstrate that “U-scores” are much more effective than dominance when tasked with identifying the better of two algorithms. We validate U-scores by having them determine the winners of the CEC 2022 competition on single objective, bound-constrained numerical optimization.…”
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Power-constrained system-on-a-chip test scheduling using a genetic algorithm
Published 2006“…This paper presents an efficient approach for the test scheduling problem of core-based systems based on a genetic algorithm. The method minimizes the overall test application time of a system-on-a-chip through efficient and compact test schedules. …”
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Teaching–learning-based optimization algorithm: analysis study and its application
Published 2024Subjects: “…Teaching–learning-based optimization algorithm…”
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Tabu search based circuit optimization
Published 1998“…In this paper we address the problem of optimizing mixed CMOS/BiCMOS circuits. The problem is formulated as a constrained combinatorial optimization problem and solved using an tabu search algorithm. …”
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Meta-Heuristic Procedures for the Multi-Resource Leveling Problem with Activity Splitting
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Application of Red Deer Algorithm in Optimizing Complex functions
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An evolutionary algorithm for solving the geometrically constrained site layout problem
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms
Published 2025“…Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems
Published 2023“…The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). …”
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CFD Based Airfoil Shape Optimization for Aerodynamic Drag Reduction
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The buffered work-pool approach for search-tree based optimization algorithms
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Genetic Algorithm for Solving Site Layout Problem with Unequal-Size and Constrained Facilities
Published 2002“…loosely versus tightly constrained layouts. In most problems considered where the total-objects-to-site-area ratio did not exceed 60%, the algorithm returned close to optimal solutions in a reasonable time.…”
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