Local search algorithms for memetic algorithms: understanding behaviors using biological intelligence

Luca, Beatrice; Craus, Mitică

doi:10.1109/icstcc.2018.8540690

Cited by 2 publications

(2 citation statements)

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“…In most cases, this can be achieved by getting the best (guiding function) from new population (the so-called "plus" refresh strategy), or simply by capturing the best individual of the new pop and recording them. In pop to complete from the smallest ("comma" strategy) [21][22][23][24][25][26].…”

Section: Figure 1 the Generational Templatementioning

confidence: 99%

“…MA relates to a metaheuristics family whose primary theme is hybridization and is fundamentally interested in exploiting all accessible information about the issue under study [20]. Each local search algorithm investigates distinct processes for reaching individuals [22]. The suggested algorithm contains nine comprehensive steps:…”

Section: Proposed Memetic Algorithm For Mompsmentioning

confidence: 99%

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Solving multi-objective master production schedule problem using memetic algorithm

Sadiq

Abdulazeez

Haron

2020

IJEECS

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<span>A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. </span>

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