In this paper we investigate the performance of simulated annealing (SA) and GENETIC ALGORITHM (GA) in preventive part replacement for minimum downtime maintenance planning. Therefore some evaluation criteria are explained in order to analyze the performance of the ALGORITHMs. So it can be decided which ALGORITHM is more suitable to apply in preventive part replacement.

This paper presents a novel Meta-Heuristic method for solving an extended Markowitz Mean–Variance portfolio selection model. The extended model considers Value-at-Risk (VaR) as risk measure instead of Variance. Depending on the method of VaR calculation its minimizing methodology differs. if we use Historical Simulation which is applied in this paper then the curve would be nonconvex.On the other hand the Mean-VaR model here includes three sets of constraints: bounds on holdings, cardinality and minimum return which cause a Mixed Integer Quadratic Programming Problem. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio’s equal to a predefined number.Because of above mentioned reasons, in this paper, we propose a new Meta- Heuristic approach based on combined Ant Colony Optimization (ACO) method and GENETIC ALGORITHM (GA). The computational results show that the proposed Hybrid ALGORITHM has the ability to optimized Mean-VaR portfolio for small portfolio.

In scheduling, from both theoretical and practical points of view, a set of machines in parallel is a setting that is important. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view, the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program is necessary because the members of the program are performed in a parallel fashion, and this performance is executed according to some precedence relationship. This paper shows the problem of allocating a number of non-identical tasks in a multi-processor or multicomputer system. The model assumes that the system consists of a number of identical processors, and only one task may be executed on a processor at a time. Moreover, all schedules and tasks are non-preemptive.

This paper presents a novel Meta-Heuristic method for solving an extended Markowitz Mean Variance portfolio selection model. The extended model considers Value-at-Risk (VaR) as risk measure instead of Variance. Depending on the method of VaR calculation its minimizing methodology differs. If we use Historical Simulation which is applied in this paper then the curve would be non-convex.On the other hand the Mean VaR model here includes three sets of constraints: bounds on holdings, cardinality and minimum return which cause a Mixed Integer Quadratic Programming Problem. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio’s equal to a predefined number.Because of above mentioned reasons, in this paper, we propose a new Meta Heuristic approach based on combined Ant Colony Optimization (ACO) method and GENETIC ALGORITHM (GA). The computational results show that the proposed Hybrid ALGORITHM has the ability to optimized Mean VaR portfolio for small portfolio.

Introduction: Drugs are mainly delivered to the target tissues by plasma proteins, such as human serum albumin, in the human body. Practical information about the thermodynamic parameters of drugs and their stability can be obtained using simulation methods, such as molecular docking. Material & Methods: This study, investigated the molecular docking of human serum albumin with fluorouracil anticancer drug. Moreover, partial charges on serum albumin protein atoms and fluorouracil atoms were calculated in this study. The best configuration was also searched using the Lamarckian GENETIC ALGORITHM. The dimensions of the grid maps were selected to be about 40 * 40 * 40 angstroms with a distance of 0. 375 angstroms. The number of GENETIC ALGORITHMs and the number of studies were adjusted to about 100 and 2. 5 million, respectively. In the end, the best performed interaction configurations with the least amount of free energy were selected. Ligplot and VMD graphic software were used to view the performed docking. Findings: In the best model, fluorouracil is able to bind to the human serum albumin protein HSA four hydrogen bonds via nitrogen and oxygen atoms with two amino acids tyrosine, one amino acid histidine and one amino acid arginine. The estimation of the free bond energies (kcal/mol) for the best model was-5. 1. Negative Gibbs free energy values (Δ, G °, ) indicated a spontaneous process, and a constant binding value (Ka ≈,109 L •,mol-1) demonstrated the optimal biological distribution of the drug in the blood plasma. Discussion & Conclusion: The docking study of the proposed models shows that fluorouracil has an aliphatic ring and hydrophobic fractions and therefore it has a high ability to form hydrophobic interactions with major amino acids at the active site of serum albumin protein.

Many real-world issues have multiple conflicting objectives, and optimization of the contradictory objectives is very difficult. In the recent years, the Multi-objective Evolutionary ALGORITHMs (MOEAs) have shown a great performance in order to optimize such problems. Thus the development of MOEAs will always lead to the advancement of science. The Non-dominated Sorting GENETIC ALGORITHM II (NSGAII) is considered as one of the most used evolutionary ALGORITHMs, and many MOEAs such as the Sequential Multi-Objective ALGORITHM (SEQ-MOGA) have emerged to resolve the NSGAII problems. SEQ-MOGA presents a new survival selection that arranges the individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this work, the Archive Sequential Multi-Objective ALGORITHM (ASMOGA) is proposed in order to develop and improve SEQ-MOGA. ASMOGA uses the archive technique in order to save the history of the search procedure so that the maintenance of the diversity in the decision space is adequately satisfied. In order to demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing the benchmark functions and designing the I-Beam problem. The optimization results are evaluated by the performance metrics such as the hyper-volume, generational distance, spacing, and t-test (a statistical test). Based on the results obtained, the superiority of the proposed ALGORITHM is clearly identified.

In this paper, echo cancellation is done using GENETIC ALGORITHM (GA). The GENETIC ALGORITHM is implemented by two kinds of crossovers, heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and different types of crossovers which are all satisfactory. Reverse SNR is used as the fitness function. It can estimate an echo path with definite length of impulse response with an adaptive filter with desired length.Results show that the proposed combined GA-LMS method operates more satisfactory than simple GA in terms of the number of generations needed to achieve a particular amount of echo cancellation. Different tests show that GAs running with heuristic crossover converge faster than GAs with microbial crossover. Results are also compared with LMS ALGORITHM. Although LMS is faster, but its solutions are less precise and it diverges in some cases. But our proposed method always converges.

This research presents new ALGORITHMs for sub transmission simultaneous substation and network expansion planning.  Given an existing system model, the projected load growth in a target year and various system expansion options, the ALGORITHMs find the optimal mix of system expansion options to minimize the cost function subject to various system constraints and single contingencies on lines and transformers.The system expansion options considered include building new sub transmission lines/substations, the capacity to be upgraded and the service area of HV/MV substations. In this research, GENETIC ALGORITHM (GA) with new coding, Ant Colony ALGORITHM (AC) and hybrid Ant Colony and GENETIC ALGORITHM (AC&GA) methods are employed. The optimization results are compared with successive elimination method to demonstrate the performance improvement.