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The conjugate gradient (CG) procedure is among the widely studied techniques for modelling problems in the areas of deep learning, machine learning, image restoration, neural networks and many more. This is due to their robust convergence and less memory requirements. Recently, numerous modifications of the CG formulas have been presented in literature. However, many of these modifications are very complex with complicated algorithms, while some formulas do not satisfy the descent property or converge globally for general nonlinear functions. In this study, we define a descent modification of the CG formula for unconstrained optimization and blurred image reconstruction. An interesting feature of our formula is that it possesses the descent properties under some mild assumptions. To demonstrate the efficiency and robustness of the new technique, the study considered a set of unconstrained optimization functions and image restoration models. Results from the experimentation shows that the proposed algorithm outperformed other existing algorithms with similar structures on unconstrained optimization problems and reconstructed blurred images with the best accepted quality. [ABSTRACT FROM AUTHOR]

An evaluating process that involves the multi parameters and rules is complex and complicated. Such complexity shows that the problem is a semi-unstructured problem. Thus, the problem faced is possibly solved by proposing a new intelligentdecision model to get the quantitative decision. Here, the experimental research was conducted by methodically operating the conception of multi-stage rule-based fuzzy logic. The experimental research is a Python programming language experiment and it is one implementation of the multi-stage rule-based fuzzy logic in solving the problem of a simple case for evaluating student performance. The evaluation model was designed thru an object-oriented approach and was conclusively able to show the result of100 dummy students' performance values. [ABSTRACT FROM AUTHOR]

Quadratic Assignment Problem (QAP) is one of the famous combinatorial optimization problems in operational research field. In this problem, a set of facilities is assigned to a set of locations in form of one-to-one assignment with the goal to obtain minimum assignment cost. Since QAP is classified under NP-hard problem, a metaheuristics approach is more appropriate for finding a good solution especially when the problem size increased. Sine Cosine Algorithm (SCA) can be categorized under population-based method which shown such an excellent performance in solving various optimization problem. SCA came up with high quality solutions and performed well due to its ability in stabilizing between exploration and exploitation phases evenly during the search process for optimal solution. However, this approach is rarely used to solve discrete optimization problem such as QAP. This is because the nature of its solution search which produce continuous values for new solution based on the concept of sine cosine principle makes it challenging in solving discrete optimization problem. Hence, this study aims to modify the SCA on its acceptance criteria based on the concept of sine cosine to solve discrete optimization problem, to solve QAP and evaluate its solution performance. The instances from QAPLIB were tested. The computational results shows that the modified SCA is an effective and superior method in solving QAP when compared to the best-known solutions presented in previous studies. [ABSTRACT FROM AUTHOR]

Evacuation is a vital step in emergency management, in which people are transported from danger to safety to reduce or avoid casualties i.e. injuries or death. During evacuation, shelters become crowded to victims and this could be chaotic. The present assignment of victims to shelters is performed manually by the rescue team and this requires much time and massive manpower to ensure good distribution. Hence, a viable system is sought in assigning victims to shelters so as to provide better victims distribution. In this paper, a mathematical model, particularly binary integer programming (BIP) model, was formulated to solve a real case study of flood evacuation assignment issue that took place in southern Malaysia. The BIP model can be applied to assign flood victims to the available shelters subject to problem constraints with the objective of minimizing the total distance. The real data used in this study involved 33 families of flood victims selected from six available shelters. The optimal solution obtained from the BIP model showed that two families were assigned to Shelter 1, while shelters 2 until 4 were assigned with seven families each, six families to Shelter 5, and the remaining four families were assigned to Shelter 6 with the total distance of 13.61 km. The optimal solution is similar with the solution implemented by the rescue team that was devised manually. However, by using the BIP model, the solution retrieved from the model can easily be obtained and saves much of the precious time amongst the rescue teams. [ABSTRACT FROM AUTHOR]

The rapid changes of lifestyles especially eating habits of fast-food and convenience food which are usually rich in sodium and sugar or carbohydrate lead to other chronic diseases especially for diabetics. Diet practice is one of the very important steps that the diabetic patients can take care of in order to avoid to other complications. The diet planning of a diabetic patient aims to balance up the nutrients and to control the level of blood glucose in the body with minimal cost. This paper aims to provide a combination of food menu that satisfies the daily nutrient requirements of a diabetic person at a minimum cost. An integer programming model of a diabetic diet problem is developed to determine the food combination that satisfies the standard daily nutritional requirements for two days and repetition of menu is prohibited. In this study, a sensitivity analysis is performed by substituting meals. It is found that the best combination of menu for two days for a diabetic patient is at the cost of RM49.10. The optimal solution for the two days menu plan fulfils the standard nutritional requirements for diabetic patients. It is suggested that the findings can be used as a guideline for the diabetic patients themselves or for hospital management to suggest meal planning for diabetic patients. [ABSTRACT FROM AUTHOR]

This paper compares the workload of the drivers for a waste collection benchmark problem. The problem involves ten data sets with different number of customers to be served and different number of disposal facilities available. Previous studies proposed a heuristic algorithm, namely Different Initial Customer (DIC) to solve the problem by constructing initial vehicles routes for the drivers with two main objectives; to minimize the total distance travelled and to minimize the total number of vehicles needed to collect the waste. The results from DIC compared well with other solutions in the literature. However, the balance of the workload among the vehicle drivers is not considered in the solutions. Thus in this paper, we evaluate the quality of the solutions in terms of the total number of customers served by each driver. Then the computational result is compared in terms of the total distance travelled which have been presented in a previous study. Comparison results show that the workload of the drivers are unbalance in terms of these two factors that may cause dissatisfaction among the drivers as well as to the managament. [ABSTRACT FROM AUTHOR]