Search Results - (( global optimization problem algorithm ) OR ( evolution optimisation based algorithm ))
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Tree physiology optimization in constrained optimization problem
Published 2018“…Metaheuristic algorithms are proven to be more effective on finding global optimum in numerous problems including the constrained optimization area. …”
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Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays
Published 2023“…Animals; Antenna arrays; Antennas; Beam forming networks; Beamforming; Behavioral research; Bioinformatics; Evolutionary algorithms; Global optimization; Problem solving; Swarm intelligence; Bio-inspired algorithms; Co-operative behaviors; Global optimization problems; Meta heuristics; Optimization algorithms; Optimization techniques; Real-parameter optimization; Swarm algorithms; Optimization…”
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Normative Fish Swarm Algorithm For Global Optimization With Applications
Published 2019“…Referred to as Normative Fish Swarm Algorithm (NFSA), the proposed Fish Swarm Algorithm, Optimized by Particle Swarm Optimization with Extended Memory (PSOEM-FSA) is expanded by amalgamating the normative knowledge to provide supplementary guidelines for better global optimum achievement and convergence rate. …”
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Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design
Published 2014“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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Performance comparison between genetic algorithm and ant colony optimization algorithm for mobile robot path planning in global static environment / Nohaidda Sariff
Published 2011“…The problem is to find the global path that satisfies the optimization criteria, which are shorter path length and less computation time. …”
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An improved genetic bat algorithm for unconstrained global optimization problems
Published 2020“…Metaheuristic search algorithms have been in use for quite a while to optimally solve complex searching problems with ease. …”
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A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
Published 2020“…The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. …”
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Hybridizing Invasive Weed Optimization with Firefly Algorithm for Unconstrained and Constrained Optimization Problems
Published 2017“…This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm for global optimization problems. …”
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Fuzzy adaptive emperor penguin optimizer for global optimization problems
Published 2023“…A test suite of twelve benchmark test functions and three global optimization problems: Team Formation Optimization (TFO), Low Autocorrelation Binary Sequence (LABS), and Modified Condition/ Decision coverage (MC/DC) test case generation problem were solved using the proposed algorithm. …”
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A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems
Published 2018“…However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. …”
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An efficient method for determining all the extreme points of function with one variable
Published 2014“…The algorithm is directly suitable for a class of problems of commonly used in solving the global optimization problems. …”
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Global optimization method for continuous - Time sensor scheduling
Published 2010“…We consider a situation in which several sensors are used to collect data for signal processing since operating multiple sensors simultaneously canses system interference, only one sensor can be active at any one time.The problem of scheduling a discrete-valued optimal control problem.This problem cannot be solved using conventional optimization problem.The Transformed problem is then decomposed into a bi-level optimization problem, which is solved using a discreate filled function method in conjunction with a conventional optimal control algorithm.Numerical results show that our algorithm is robust, efficient, and reliable in attaining a near globally optimal solution.…”
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Comparative study of meta-heuristics optimization algorithm using benchmark function
Published 2017“…Therefore it is necessary to compare the performance of these algorithms with certain problem type. This paper compares 7 meta-heuristics optimization with 11 benchmark functions that exhibits certain difficulties and can be assumed as a simulation relevant to the real-world problems. …”
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Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis
Published 2014“…To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. …”
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Artificial immune system based on hybrid and external memory for mathematical function optimization
Published 2023Subjects:Conference Paper -
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A Novel Discrete Filled Function Algorithm in Solving Discrete Optimization Problems (S/O: 12408)
Published 2016“…Several global methods have been proposed for solving discrete optimization problems. …”
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Global Algorithms for Nonlinear Discrete Optimization and Discrete-Valued Optimal Control Problems
Published 2009“…We then consider the task of determining near globally optimal solutions of discrete-valued optimal control problems. …”
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A carnivorous plant algorithm for solving global optimization problems
Published 2021“…Experimental simulations demonstrated the supremacy of the CPA in solving global optimization problems.…”
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A competitive co-evolutionary approach for the nurse scheduling problem
Published 2026“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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