Efrén Mezura-Montes

Bio: Efrén Mezura-Montes is an academic researcher from Universidad Veracruzana. The author has contributed to research in topics: Metaheuristic & Optimization problem. The author has an hindex of 7, co-authored 13 publications receiving 152 citations.

Differential evolution with combined variants for dynamic constrained optimization

Abstract: In this work a differential evolution algorithm is adapted to solve dynamic constrained optimization problems. The approach is based on a mechanism to detect changes in the objective function and/or the constraints of the problem so as to let the algorithm to promote the diversity in the population while pursuing the new feasible optimum. This is made by combining two popular differential evolution variants and using a memory of best solutions found during the search. Moreover, random-immigrants are added to the population at each generation and a simple hill-climber-based local search operator is applied to promote a faster convergence to the new feasible global optimum. The approach is compared against other recently proposed algorithms in an also recently proposed benchmark. The results show that the proposed algorithm provides a very competitive performance when solving different types of dynamic constrained optimization problems.

Optimum synthesis of a four-bar mechanism using the modified bacterial foraging algorithm

Abstract: This paper presents the mechanical synthesis of a four-bar mechanism, its definition as a constrained optimisation problem in the presence of one dynamic constraint and its solution with a swarm intelligence algorithm based on the bacteria foraging process. The algorithm is adapted to solve the optimisation problem by adding a suitable constraint-handling technique that is able to incorporate a selection criterion for the two objectives stated by the kinematic analysis of the problem. Moreover, a diversity mechanism, coupled with the attractor operator used by bacteria, is designed to favour the exploration of the search space. Four experiments are designed to validate the proposed model and to test the performance of the algorithm regarding constraint-satisfaction, sub-optimal solutions obtained, performance metrics and an analysis of the solutions based on the simulation of the four-bar mechanism. The results are compared with those provided by four algorithms found in the specialised literature used to solve mechanical design problems. On the basis of the simulation analysis, the solutions obtained by the proposed algorithm lead to a more suitable design based on motion generation and operation quality.

Performance comparison of local search operators in differential evolution for constrained numerical optimization problems

Abstract: This paper analyzes the relationship between the performance of the local search operator within a Memetic Algorithm and its final results in constrained numerical optimization problems by adapting an improvement index measure, which indicates the rate of fitness improvement made by the local search operator. To perform this analysis, adaptations of Nealder-Mead, Hooke-Jeeves and Hill Climber algorithms are used as local search operators, separately, in a Memetic DE-based structure, where the best solution in the population is used to exploit promising areas in the search space by the aforementioned local search operators. The "-constrained method is adopted as a constraint-handling technique. The approaches are tested on thirty six benchmark problems used in the special session on "Single Objective Constrained Real-Parameter Optimization" in CEC'2010. The results suggest that the algorithm coordination proposed is suitable to solve constrained problems and those results also show that a poor value of the improvement index measure does not necessarily reflect on poor final results obtained by the MA in a constrained search space.

Recent advances in differential evolution – An updated survey

Abstract: Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Ensemble strategies for population-based optimization algorithms – a survey

Abstract: In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constraint handling method, local search method, surrogate model, niching method, etc. In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, tournament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on. In POA literature, No Free Lunch (NFL) theorem has been well-documented and therefore, to effectively solve a given optimization problem, an appropriate configuration is necessary. But, the trial and error approach for the appropriate configuration may be impractical because at different stages of evolution, the most appropriate configurations could be different depending on the characteristics of the current search region for a given problem. Recently, the concept of incorporating ensemble strategies into POAs has become popular so that the process of configuring an optimization algorithm can benefit from both the availability of diverse approaches at different stages and alleviate the computationally intensive offline tuning. In addition, algorithmic components of different advantages could support one another during the optimization process, such that the ensemble of them could potentially result in a versatile POA. This paper provides a survey on the use of ensemble strategies in POAs. In addition, we also provide an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. and compare them with the ensemble strategies in the context of POAs.