Now showing 1 - 4 of 4
  • Publication
    A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem
    Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to findthe best routes with minimum cost for a number of vehicles serving a number of scattered customersunder some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routingproblem, metaheuristic optimization algorithms are widely used for tackling this type of challenge.Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm thatmimics the COVID-19 herd immunity treatment strategy. In this paper, CHIO is modified for capacitatedvehicle routing problem. The modifications for CHIO are accomplished by modifying its operators to pre-serve the solution feasibility for this type of vehicle routing problems. To evaluate the modified CHIO, twosets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMPdata set which has 27 instances with different models. Moreover, the results achieved by modified CHIOare compared against the results of other 13 well-regarded algorithms. For the first data set, the modifiedCHIO is able to gain the same results as the other comparative methods in two out of ten instances andacceptable results in the rest. For the second and the more complicated data sets, the modified CHIO isable to achieve very competitive results and ranked the first for 8 instances out of 27. In a nutshell,the modified CHIO is able to efficiently solve the capacitated vehicle routing problem and can be utilizedfor other routing problems in the future such as multiple travelling salesman problem
    Scopus© Citations 4  131  54
  • Publication
    A Review for the Genetic Algorithm and the Red Deer Algorithm Applications
    The Red Deer algorithm (RD), a contemporary population-based meta heuristic algorithm, applications are thoroughly examined in this paper. The RD algorithm blends evolutionary algorithms' survival of the fittest premise with the productivity and richness of heuristic search approaches. On the other a well-known and relatively older evolutionary based algorithm called the Genetic Algorithm applications are also shown. The contemporary algorithm; the RDA, and the older algorithm; the GA have wide applications in computer science and engineering. This paper sheds the light on all those applications and enable researchers to exploit the possibilities of adapting them in any applications they may have either in engineering, computer science, or business.
  • Publication
    A Review of the Genetic Algorithm and JAYA Algorithm Applications
    This study throws the light on two metaheuristic algorithms and enable researchers to leverage the potential of adapting them in whatever applications they may have either in engineering, computer science, or business. The two algorithms are the GA and the JAYA. The JAYA algorithm is a modern population-based meta heuristic algorithm, its applications are presented in this work. The JA Y A algorithm integrates evolutionary algorithms' survival of the fittest concept with the productivity and richness of heuristic search methodologies. On the other a well-known and somewhat older evolutionary based method called the Genetic Algorithm with applications is also presented here. The recent two algorithms; the JA Y A and the GA have broad comparable applications in computer science and engineering applications.
  • Publication
    An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications
    (2022) ;
    Al‑Betar, Mohammed Azmi
    Awadallah, Mohammed A.
    Abu Doush, Iyad
    Assaleh, Khaled
    In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.
    Scopus© Citations 11  114  32