Ant colony optimization based energy efficiency for improving opportunistic routing in multimedia wireless mesh network

Harikishore, S.; Sumalatha, V.

doi:10.11591/ijeecs.v16.i3.pp1371-1378

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Despite the positive characteristics of LA and its successful application to address various problems, it still suffers from slow convergence problem when addressing constraint problems especially TOPTW. As any population-based metaheuristic [23][24][25], this problem is attributed to the fact that LA is effective in exploring the search space but ineffective in exploiting it. In order to improve this issue, an Improved Lion Algorithm (ILA) is proposed.…”

Section: Improved Lion Algorithmmentioning

confidence: 99%

Lion optimization algorithm for team orienteering problem with time window

Yassen

Jihad

Abed

2021

IJEECS

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<span>Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.</span>

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Section: Improved Lion Algorithmmentioning

confidence: 99%

Lion optimization algorithm for team orienteering problem with time window

Yassen

Jihad

Abed

2021

IJEECS

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“…The ant colony optimization (ACO) is a meta-heuristic technique that mimics the foraging behavior of ant colonies, which is consist of seeking the shortest path between their nests and food sources. ACO was initially employed to solve graph related problems, like the traveling salesman problem (TSP) [21], the opportunistic routing [22] and the optimal power flow [23]. To solve such problems, ants randomly choose the vertex to be visited.…”

Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

An optimal design of square spiral integrated inductor using metaheuristic techniques

Abi

Bouyghf

Benhala

et al. 2020

IJEECS

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<p>In this paper, the optimal sizing of CMOS RF square spiral integrated inductor utilizing three meta-heuristic techniques namely Ant Colony Optimization, Artificial Bee Colony and Differential Evolution is presented. The π-model is employed for the characterization of inductor behavior. In this optimization procedure, the geometrical parameters of the CMOS RF square spiral integrated inductor are considered as the design variables that satisfy the most important constraints such as the fixed value of required inductance 4nH at the operating frequency 2.4 GHz. The design of the integrated square spiral inductor is done with UMC 130 nm CMOS technology. A comparison between the used meta-heuristic techniques is emphasized. The optimization results are checked and validated by the mean of the Momentum Advanced Design System (ADS).</p>

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