An evolutionary hybrid algorithm for complex optimization problems

Zemzami, Maria; Elhami, Norelislam; Itmi, Mhamed; Hmina, Nabil; LGS-ENSA-Kenitra, Morocco

doi:10.30534/ijatcse/2019/05822019

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…Though these questions have been present since quite some time, they have not found conclusive answers so far. Also there is continuous research going on in the field of optimization algorithms suggesting modifications in existing algorithms and/or their hybrids such as [52], [53] etc. and this would further encourage studies of their application to database query optimization.…”

Section: Discussionmentioning

confidence: 99%

Database Query Optimization using Genetic Algorithms: A Systematic Literature Review

Chande¹

2019

IJATCSE

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As data is escalating exponentially efficient storage and fast retrieval of data are increasingly becoming areas of significance for researchers. Query optimization is probably the most researched problem in the database domain. Among the solutions to this problem, Genetic Algorithms have proved to be a viable alternative. This systematic literature review explores work done on the application of Genetic Algorithm to optimization of database queries to understand how its use for database query optimization has taken shape and to identify the open questions. It could be inferred that Join-Order, Distributed databases and Hybrids with Genetic Algorithm are sustaining researchers' interest.

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Section: Discussionmentioning

confidence: 99%

Database Query Optimization using Genetic Algorithms: A Systematic Literature Review

Chande¹

2019

IJATCSE

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“…Link-Order Criterion LO1 produces links (a, c, f, h, e, g, b, d) in increasing cost order, e.g., link a with cost of 2 then link c with cost of 3. You can use any sorting algorithm, e.g., merge sort, for any link order with time complexity of O(|E|×log |E|) [29], [30], [31].…”

Section: Improving the Efficiency Of Dpcr-l Using Linkmentioning

confidence: 99%

On Minimizing Cost of Reliable Network Topological Design using a Practical DP Approach

Elshqeirat¹

2020

IJATCSE

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This paper addresses an NP-hard problem called NTD-CR that using dynamic programming (DP) scheme. This problem referred to as Network Topology Design with minimum Cost subject to a Reliability constraint. It designs a minimal-cost communication network topology that satisfies a pre-defined (s, t) terminal reliability constraint. Our DP approach, called DPCR-L, mainly select the set of possible links to be deleted from the original network to generate an optimal Network Topology. The NTD-CR design problem aims to find a Network Topology that has minimal cost with the required reliability for the network. Five link-ordering criteria are proposed to improve the performance of DPCR-L. Each greedy heuristic order allows DPCR-L to enumerate the selected deleted links, which improves the time complexity while producing near optimal topology. Extensive Simulations based on different benchmark networks of various sizes are used to compare DPCR-L with existing state-of-the-art techniques and show the merits of using the ordering methods, and the effectiveness of our algorithm. Our simulations show that DPCR-L produces 93% optimal results. Interestingly, of the non-optimal results, DPCR-L produces a network with reliability no worse than 5.38% off optimal, and most of the non-optimal results have a lower cost than that for optimal up to 0.17%. Typically, for the most of the network topologies, DPCR-L generates NT with the same or better 2-terminal reliability measure and speeds up its running time up to 31.71%. Furthermore, simulation results on large size networks show that DPCR-L speeds up the process with up to (47.28%) compared to the recent existing approach. Finally, the results present the applicability of DPCR-L on networks containing a large number of links and demonstrated better performance and computationally more efficient than other existing algorithms.

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“…Artificial intelligence (AI) is widely used in the field of power systems. Among the techniques used are evolutionary programming (EP) [11][12][13][14], particle swarm optimization (PSO) [15][16][17][18], moth flame optimization (MFO) [19][20][21][22], and whale optimization algorithm (WOA) [23][24][25]. EP is developed on the basis of biological evolution.…”

Section: Introductionmentioning

confidence: 99%

Solving Non-Smooth Economic Load Dispatch Problem via Flower Pollination Algorithm

2020

IJETER

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This paper discusses the generator optimization method for non-smooth economic dispatch (ED) in power systems using the Flower Pollination Algorithm (FPA). The generating value of each unit is determined without compromising the power limits of each generator and the amount of energy demand. The objective of this study is to provide the minimumgenerating costs based on the seamless cost function of the ED. The feasibility of the FPA method is compared with particle swarm optimization (PSO) and moth flame optimizer (MFO). Two types of power system networks, 10and 40-generators, are tested using MATLAB. Simulation results show that compared with PSO and MFO,FPA provides better results in optimizing energy generation with minimum generation cost and power loss.

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An evolutionary hybrid algorithm for complex optimization problems

Cited by 12 publications

References 11 publications

Database Query Optimization using Genetic Algorithms: A Systematic Literature Review

Database Query Optimization using Genetic Algorithms: A Systematic Literature Review

On Minimizing Cost of Reliable Network Topological Design using a Practical DP Approach

Solving Non-Smooth Economic Load Dispatch Problem via Flower Pollination Algorithm

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