A Comparative Study of Meta-Heuristic Optimization Algorithms for 0 – 1 Knapsack Problem: Some Initial Results

Ezugwu, Absalom E.; Pillay, Verosha; Hirasen, Divyan; Sivanarain, Kershen; Govender, Melvin

doi:10.1109/access.2019.2908489

Cited by 74 publications

(34 citation statements)

References 60 publications

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“…In [42], the authors tested the hybridized Genetic Algorithm and Simulated Annealing (IGASA) to get rid of the limitations of GA and SA. The hybrid algorithm (IGASA) has been used for solving both low-dimensional and high-dimensional knapsack problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Group-Based Cooperative Optimization Algorithm

et al. 2020

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Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Group-Based Cooperative Optimization Algorithm

et al. 2020

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“…More so, these proportions describe what should be the state of blood stocked in the bank in an ideal situation. Relating the population of the donor types to the amount of each blood type available expressed by equations (39)(40)(41)(42)(43)(44)(45)(46), the following equations are obtained:…”

Section: B the Dynamic Mass Balance Systemmentioning

confidence: 99%

“…The hybrid optimization algorithm proposed in this paper consists of three well-known global metaheuristics, namely SOS, GA, and PSO. Further, these three algorithms have been proven to be highly effective, efficient, and robust in solving very complex global optimization problems [41]. Of note is the aforementioned individual algorithms, each having been previously used separately to solve the blood assignment problem, with their respective performance studies having been reported in [10], [26], [31].…”

Section: E Symbiotic Organism Search Genetic Particle Swarm Optimizamentioning

confidence: 99%

“…They have: (1) operators for exploration and exploitation techniques; GA, PSO, and SOS possess these functionalities; (2) parameter tuning or reduction concepts, SOS has only one main parameter which is the swarm population size; (3) adaptability to the solution of a wide range of problems, which pertains to the three chosen algorithms; and (4) stagnation prevention techniques [44]. An additional criterion was that the selected metaheuristic should also have been reported to show superior performance over the traditional heuristic approaches [41], [45], for example, the GA and PSO have good performance track records [34], [41]. The primary reason the SOS algorithm was selected was due to its novelty, and nevertheless, its improved functionality and suitability are worth investigating for the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

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Computational Intelligence Approach to Dynamic Blood Allocation With ABO-Rhesus Factor Compatibility Under Real-World Scenario

et al. 2020

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The supply chain management of blood products specifically deals with the aspect of efficient planning, implementing, and controlling of the in and out flow processes of blood unity in the blood bank system. Therefore, any improvement in the management of these chains would directly influence the manner in which blood and blood products are distributed to all the various sectors requiring such scarce and precious resources. Generally, the management of blood products is difficult due to the four ABO blood groups, which is further complicated by positive/negative rhesus factors. In this paper, a more simplified and robust dynamic mathematical model is presented for the efficient management of the blood bank. The corresponding sets of governing equations from an existing model are extended to cover the rhesus factors and the solution methods of the newly derived equations are subsequently investigated. In addition, a mathematical representation of the decision making process is presented as a function of the blood bank stockpile. Furthermore, in order to demonstrate the robustness of the developed model and to provide managerial insights, a new global hybrid symbiotic organisms search genetic particle swarm optimization algorithm is developed. Several numerical computations are performed using real-world datasets from the Enugu National Blood Transfusion Centre, in Nigeria, which fall within the monthly initial blood volume bounds of 300 over a period of eight years (2010-2018). Finally, the experimental results show that the mathematical model and metaheuristic optimization method proposed in this paper offer a better solution approach for blood allocation in dynamic environments. More so, the impact of some essential control parameters on the results are analysed to help the blood bank managers or decision makers to select accurately the desired parameters for optimal results yield.

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“…In [11] and [12] the authors have evaluated the performance of various selection techniques. In [15] the authors evaluated various algorithmic techniques used in optimization of 0/1 knapsack.…”

Section: Background and Related Workmentioning

confidence: 99%

Computer Science & Information Technology (CS & IT)

2020

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Genetic Algorithm is an evolutionary algorithm and a metaheuristic that was introduced to overcome the failure of gradient based method in solving the optimization and search problems. The purpose of this paper is to evaluate the impact on the convergence of Genetic Algorithm vis-a'-vis 0/1 knapsack. By keeping the number of generations and the initial population fixed, different crossover methods like one point crossover and two-point crossover were evaluated and juxtaposed with each other. In addition to this, the impact of different selection methods like rank-selection, roulette wheel and tournament selection were evaluated and compared. Our results indicate that convergence rate of combination of one point crossover with tournament selection, with respect to 0/1 knapsack problem that we considered, is the highest and thereby most efficient in solving 0/1 knapsack.

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A Comparative Study of Meta-Heuristic Optimization Algorithms for 0 – 1 Knapsack Problem: Some Initial Results

Cited by 74 publications

References 60 publications

Dynamic Group-Based Cooperative Optimization Algorithm

Dynamic Group-Based Cooperative Optimization Algorithm

Computational Intelligence Approach to Dynamic Blood Allocation With ABO-Rhesus Factor Compatibility Under Real-World Scenario

Computer Science & Information Technology (CS & IT)

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