Search Results - (( case optimization strategy algorithm ) OR ( java application learning algorithm ))
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Elitism Based Migrating Birds Optimization Algorithm for Optimization Testing
Published 2017“…In this paper, we combined MBO and elitism to solve the Combinatorial Interaction Testing (CIT) problem i.e. to find a set of minimum test case which is an NP-Complete problem. This proposed strategy is the first to utilize population based metaheuristic algorithm i.e. …”
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An Orchestrated Survey on T-Way Test Case Generation Strategies Based on Optimization Algorithms
Published 2014“…This paper presents an orchestrated survey of the existing OpA t-way strategies as Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Particle Swarm Optimization based strategy (PSTG), and Harmony Search Strategy (HSS). …”
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Published 2015“…Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. …”
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Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation
Published 2018“…The VS-MGS’s results ultimately revealed that the adaptation of elitism mechanism with iteration in greedy algorithm resulted in an improved efficiency in the process of generating a near-optimal test case set size.…”
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Test Cases Minimization Strategy Based On Flower Pollination Algorithm
Published 2016“…Hence, a test cases minimization strategy is an essential to obtain an optimize test cases and consequently, time will also be reducing. …”
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Comparative Performance Analysis of Flower Pollination Algorithm and Harmony Search based strategies: A Case Study of Applying Interaction Testing in the Real World
Published 2016“…As a consequence, many t-way optimization algorithm based strategies have been designed and implemented. …”
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Whale optimization algorithm strategies for higher interaction strength t-way testing
Published 2022“…To ensure that WOA conquers premature convergence and avoids local optima for large search spaces (owing to high-order interaction), three variants of WOA have been developed, namely Structurally Modified Whale Optimization Algorithm (SWOA), Tolerance Whale Optimization Algorithm (TWOA), and Tolerance Structurally Modified Whale Optimization Algorithm (TSWOA). …”
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Hybrid Migrating Birds Optimization Strategy for t-way Test Suite Generation
Published 2019“…In the case where this strategy is not the most optimal, the resulting test size is sufficiently competitive.…”
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Assessing optimization based strategies for t-way test suite generation: The case for flower-based strategy
Published 2016“…In order to summarize the achievements so far and facilitate future development, the main focus of this paper is, first, to present a critical comparison of adoption optimization algorithms (OA) as a basis of the t-way test suite generation strategy and, second, to propose a new t-way strategy based on Flower Pollination Algorithm, called Flower Strategy (FS). …”
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Optimal load management strategy for enhanced time of use (ETOU) electricity tariff in Peninsular Malaysia / Mohamad Fani Sulaima
Published 2020“…Self-organizing mapping algorithm was integrated with the PSO, EPSO, and ACO algorithms to classify and select the best load management (LM) strategies. …”
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T-way test data generation startegy with MBO algorithm
Published 2015“…This paper presented the implementation of a nature inspired metaheuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a t-way test data generation strategy. …”
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Optimization of operations of reservoir systems for hydropower generation in Tigris River Basin, Iraq
Published 2016“…The performances of these two algorithms were evaluated through comparing their optimal values. …”
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A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…DE is developed based on an improved Genetic Algorithm and come with different strategies for faster optimization. …”
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The effect of replacement strategies of genetic algorithm in regression test case prioritization of selected test cases
Published 2015“…This study presents an optimized regression test case prioritization of selected test cases for object-oriented software using Genetic algorithm with different replacement strategies. …”
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A swarm intelligent approach for multi-objective optimization of compact heat exchangers
Published 2023Article -
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Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review
Published 2025“…Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. …”
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A hybrid kidney algorithm strategy for combinatorial interaction testing problem
Published 2022“…Recent studies have implemented hybrid metaheuristic algorithms as the basis for CIT strategy. However, the existing hybrid metaheuristic-based CIT strategies generate a competitive FTS size, there is no single CIT strategy can overcome others existing in all cases. …”
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Optimal Strategy for Comfort-Based Home Energy Management System Considering Impact of Battery Degradation Cost Model
Published 2024“…Total cost minimization, peak load shifting, and residents� thermal comfort satisfaction are combined and considered in the optimal scheduling algorithm. The impact of battery degradation costs on the charging and discharging strategy of HESS and PEV is also compared and discussed in this case study. …”
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A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
Published 2018“…Simulation results reveal the proposed algorithm’s ability to produce real and well-distributed Pareto optimum fronts for all considered multi-objective optimization cases. …”
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