The paper contains an extensive experimental study which focuses on a major idea on Target Optimization (TO) prior to the training process of artificial machines. Generally, during training process of an artificial machine, output is computed from two important parameters i.e. input and target. In general practice input is taken from the training data and target is randomly chosen, which may not be relevant to the corresponding training data. Hence, the overall training of the neural network becomes inefficient. The present study tries to put forward TO as an efficient methodology which may be helpful in addressing the said problem. The proposed work tries to implement the concept of TO and compares the outcomes with the conventional classifiers. In this regard, different benchmark data sets are used to compare the effect of TO on data classification by using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) optimization techniques.

This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.

One of the most challenging tasks in bioinformatics is the resolution of Multiple Sequence Alignment (MSA) problem. It consists in comparing a set of protein or DNA sequences, in aim of predicting their structure and function. This paper introduces a new bio-inspired approach to solve such problem. This approach named BA-MSA is based on Bat Algorithm. Bat Algorithm (BA) is a recent evolutionary algorithm inspired from Bats behavior seeking their prey. The proposed approach includes new mechanism to generate initial population. It consists in generating a guide tree for each solution with progressive approach by varying some parameters. The generated guide tree will be enhanced by Hill-Climbing algorithm. In addition, to deal with the premature convergence of BA, a new restart technique is proposed to introduce more diversification when detecting premature convergence. Balibase 2.0 datasets are used for experiments. The comparison with well-known methods as MSA-GA MSA-GA (w\prealign), ClustalW, and SAGA and recent method (BBOMP) shows the effectiveness of the proposed approach.

WiMAX is one of the broadband wireless access technologies, which provides the efficient QoS to the large number of users. The multimedia applications such as real time and non-real time services are gaining importance in the WiMAX network. To support such applications, there is a need to propose an efficient QoS traffic prediction routing protocol for the WiMAX networks. To address this, the authors are using Gene Expression Programming technique. They have generated datasets for CBR based traffic and file transfer applications. Here, they focus to develop the mathematical expressions for throughput of the network in terms of bandwidth, average end-to-end delay and average jitter for CBR based traffic and file transfer applications, so that they can analyze and predict the QoS traffic of the network. The simulation results show that the model values and the target values match with better approximation. Further, sensitivity analysis has been carried out for both CBR based traffic and file transfer applications.

A new Meta heuristic algorithm inspired of the biologic nephron performance for optimization of objective functions in Np-hard problems is introduced. The complexity of the problems increases with their size, and hence their solution space increases exponentially. Despite of designing the several search techniques with balanced exploration and exploitation in order to solve such as these problems, there are some drawbacks to make suitable adjustment between exploring and exploiting in performance of the Meta heuristic algorithms. The proposed algorithm in this paper can adjust between intensification and diversification strategies intrinsically, to make efficient optimization technique. For testing Nephron algorithm optimization (NAO), the traveling salesman problem (TSP) is provided as a solution in various sizes. Results indicate that NAO provides robust optimal solutions.

An efficient adaptive version of Melody Search algorithm (EAMS) is introduced in this study, which is a powerful tool to solve optimization problems in continuous domains. Melody search (MS) algorithm is a recent newly improved version of harmony search (HS), while the algorithm performance strongly depends on fine-tuning of its parameters. Although MS is more efficient for solving continuous optimization problems than most of other HS-based algorithms, the large number of algorithm parameters makes it difficult to use. Hence, the main objective in this study is to reduce the number of algorithm parameters and improving its efficiency. To achieve this, a novel improvisation scheme is introduced to generate new solutions, a useful procedure is developed to determine the possible variable ranges in different iterations and an adaptive strategy is employed to calculate proper parameters' values and choose suitable memory consideration rules during the evolution process. Extensive computational comparisons are carried out by employing a set of eighteen well-known benchmark optimization problems with various characteristics from the literature. The obtained results reveal that EAMS algorithm can achieve better solutions compared to some other HS variants, basic MS algorithms and certain cases of well-known robust optimization algorithms.

Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

Since ages nations have been trying to improve their military effectiveness by adopting various measures. Having an anticipatory system, which can not only accurately predict the most probable location for the enemy's base station but also finds the best route to that point, will lead to improved military operations. This paper aims to propose an integrated framework for developing an efficient anticipatory system. In the first phase of the framework, it proposes Anticipatory Multi objective Cuckoo Search (AMOCS) algorithm to identify the best probable location for deployment of enemy forces. For the second phase a hybrid CS-ACO algorithm is developed for obtaining the most suitable path to the location identified in the first phase. To test the proposed system, satellite image of regions of different terrain types namely plain/desert and mountainous respectively, are chosen. Experimental results demonstrate that the system makes accurate predictions.