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Summary: This article presents a study on the placement of multiple nulls as well as minimization of side lobe level in the radiation pattern using a planar hexagonal antenna array structure in two different vertical planes. The desired null depth is achieved to suppress the interference signal by the position‐only control of the uniformly excited isotropic antennas in the array structure. The immediate solution to the mentioned computational problem is reached by various meta‐heuristic optimization algorithms such as teaching learning‐based optimization (TLBO), symbiotic organism search (SOS), and moth fly optimization (MFO) within a considerably reduced processing time with a control over the design constrains. Various examples for diversified scenarios are demonstrated to place multiple deep nulls in the radiation pattern without compromising the pattern constraints and all other radiation pattern characteristics. This experiment sought to illustrate and quantify the unique benefits and limitations of proposed technique using three considered meta‐heuristic optimization algorithm. [ABSTRACT FROM AUTHOR]

Summary: There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods. [ABSTRACT FROM AUTHOR]

SUMMARY Cells assignment to switches plays an important role in the area of resource management in cellular network. Efficient homing of cells to switches can have a significant impact on handoff and cabling costs. This problem is an NP-hard problem, which cannot be solved by conventional optimization algorithms. It is therefore necessary to use metaheuristic methods to solve it. This research proposes a hybrid heuristic approach for further improving the quality of solutions. This approach applies API ant algorithm in combination with one of the best heuristics used to solve this problem: Tabu Search. A key element in the success of this hybrid approach is the use simultaneously an intensified research on several areas of research space. The implementation of these algorithms has been subject to extensive tests. In comparison with previous research, the proposed method is efficient, regarding quality of solutions provided for moderate-sized and large-sized cellular mobile networks. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]