Invited paper: A Review of Thresheld Convergence

A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers

Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization

Memetic computation (MC) has emerged recently as a new paradigm of efficient algorithms for solving the hardest optimization problems. On the other hand, artificial bees colony (ABC) algorithms demonstrate good performances when solving continuous and combinatorial optimization problems. This study tries to use these technologies under the same roof. As a result, a memetic ABC (MABC) algorithm has been developed that is hybridized with two local search heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction exploitation (RWDE). The former is attended more towards exploration, while the latter more towards exploitation of the search space. The stochastic adaptation rule was employed in order to control the balancing between exploration and exploitation. This MABC algorithm was applied to a Special suite on Large Scale Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary Computation. The obtained results the MABC are comparable with the results of DECC-G, DECC-G*, and MLCC.Comment: CONFERENCE: IEEE Congress on Evolutionary Computation, Brisbane, Australia, 201

Detection of Buried Inhomogeneous Elliptic Cylinders by a Memetic Algorithm

The application of a global optimization procedure to the detection of buried inhomogeneities is studied in the present paper. The object inhomogeneities are schematized as multilayer infinite dielectric cylinders with elliptic cross sections. An efficient recursive analytical procedure is used for the forward scattering computation. A functional is constructed in which the field is expressed in series solution of Mathieu functions. Starting by the input scattered data, the iterative minimization of the functional is performed by a new optimization method called memetic algorithm. (c) 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Simulated annealing with thresheld convergence

Stochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing probabilistically allows "backward" steps which facilitate exploration. However, the balance between exploration and exploitation in simulated annealing is biased towards exploitation - improving moves are always accepted, so local (greedy) search steps can occur at even the earliest stages of the search process. The purpose of "thresheld convergence" is to have these early-stage local search steps "held" back by a threshold function. It is hypothesized that early local search steps can interfere with the effectiveness of a search technique's (concurrent) mechanisms for global search. Experiments show that the addition of thresheld convergence to simulated annealing can lead to significant performance improvements in multi-modal search spaces.IEEE Computational Intelligence Societ

Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page