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In most of the optimization studies, the problem related data is assumed to be exactly known beforehand and remain stationary throughout whole optimization process. However, majority of real life problems and their practical applications are dynamic in their nature due to the reasons arising from unpredictable events, such as rush orders, fluctuating capacities of manufacturing constraints, changes...
This paper presents an experimental study on collaborative search by distributed autonomous agents. We consider a problem such that multiple agents search for target objects in a field, and communicate with each other to exchange information of objects. Agents have six different strategies: cooperative, skeptical, free rider, liar, skeptical liar and solitary. These strategies characterize behaviors...
In this paper, we propose a feasible method for solving the NP-hard absolute value equation (AVE) Ax -- |x| = b, which has 2n-solutions. The method mixes differential evolution, K-Means and Simplex Search Method together and achieves an effective balance of exploration and exploitation, denotes SKDE. SKDE execute k-means to gain 2n subgroups from initial individuals. perform DE operation to obtain...
Adaptive differential evolution algorithm based on gradient and polar coordinates search strategies (ADE) is proposed in this paper. In order to improve the precision of solutions, gradient and polar coordinates search strategies are introduced. Since the gradient search strategy generates offsprings using the derivative definition, it will accelerate the convergence speed. Polar coordinates search...
This paper aims to present an application of a recently proposed metaheuristic approach, namely bat algorithm (BA), for solving the single machine with total weighted tardiness (SMTWT) scheduling problem. In this paper, a guided population and two-swap local search are introduced to integrate with the BA. Four variants of BA methodology, including classical BA, BA with two-swap, BA with guided population,...
When krill herd (KH) is used to solve complicated multimodal functions, sometimes it fails to find the best solutions and cannot converge fast. Herein, we propose a hybrid KH method, called PBILKH, by integrating the KH with the population-based incremental learning (PBIL). In addition, a type of elitism is applied to memorize the krill with the best fitness when finding the best solution. The effectiveness...
This work proposes a novel preference based evolutionary multi and many-objective optimization approach to search a specific region of the Pareto front. First, to know the overview of the entire Pareto front, the proposed approach roughly approximates it by using a representative MOEA/D with uniformly distributed weight vectors. Then, the obtained solutions are plotted on the parallel coordinates...
Evolutionary algorithms are among the best multi-objective optimizers, but the large number of objective function evaluations they require makes it hard to use them to solve certain real-life tasks. In this work we present a surrogate-based local search for the multi-objective covariance matrix adaption evolution strategy (MO-CMA-ES). The local search is based on the estimation of hypervolume contribution...
This study proposes a novel recombination operator, called hybrid crossover operator (HX), which is performed in gene level of chromosome to enhance the optimization performance of multi-objective evolutionary algorithms (MOEAs). The proposed HX operator combines the advantages of simulated binary crossover with local search ability and differential evolution with strong global search capability....
The focus of this paper is the memory-less Gravitational Search Algorithm (GSA), which is a unique nature inspired algorithms for continuous optimization, based on the laws of gravity and laws of motion. In order to improve the efficiency, reliability and robustness of GSA, an improved GSA is presented in this paper, which incorporates a simple update mechanism of "best-so-far" particle...
In this paper, we focus on Cuckoo Search (CS) that is one of metaheuristics, and propose an adaptive CS to improve its search performance and usability. First, we analyze basically and qualitatively the effects of CS's parameter on its search dynamics. Second, from the analysis results, we define an indicator that evaluates the search state of CS based on the effective metaheuristics strategy. Moreover,...
In this work, the Linear Ordering Problem (LOP) has been approached using a discrete algebraic-based Differential Evolution for the Linear Ordering Problem (LOP). The search space of LOP is composed by permutations of objects, thus it is possible to use some group theoretical concepts and methods. Indeed, the proposed algorithm is a combinatorial Differential Evolution scheme designed by exploiting...
Genetic Programming (GP) is an evolutionary optimization method for generating tree structural programs. It is important to maintain the population diversity for preventing GP search from falling into local optima. For this purpose, we propose a new method which introduces a concept of genealogy into the population. We call the method Genetic Programming using the Best Individuals of Genealogies (GPBIG)...
In this paper, we cope with problems of forming optimal groups of students in a class for learning collaboratively. The objective of the problems is to maximize the marks of the students through the collaborative learning. We propose two genetic algorithms (GAs) in order to search for the optimal solution of the problems. In the first GA, only promising solution space is searched by using an effective...
The optimization of many objectives requires a set of optimal solutions known as Pareto solutions. Similarly to the optimization of single objective in Evolutionary Algorithms (EAs), the Multiobjective Evolutionary Algorithms (MOEAs) also suffer from loss of genetic diversity, allowing the appearance of sparse regions along the Pareto frontier. A mechanism to maintain the population diversity along...
The post enrolment based course timetabling problem (PECTP) is one type of university course timetabling problem which a set of events has to be assigned into time slots and suitable rooms according to students' enrolment data. This problem is classified as a combinatorial optimization problem and it is very hard to solve the problem efficiently because solving the problem is to find an optimal timetable...
Classification problems for imbalanced data distribution pose many challenges to standard learning algorithms as at least one class is under-represented relative to others. In this paper, we present a new approach to deal with this kind of problems, in which a multi-objective evolutionary algorithm is engaged to detect the best cost matrix to be further used by the learning algorithm in the classification...
Rank Aggregation is needed to combine many different rank orderings on the same set of alternatives, or candidates, in order to obtain a better ordering. The aim of this field is to somehow merge a number of ranked lists in order to build a single superior ranked list. Various methods exist for dealing with the problem of Rank Aggregation problem. In this paper, Rank Aggregation is implemented using...
Nature-inspired computing algorithms (NICs in short) inherit a certain length of history tracing back to Genetic Algorithm and Evolutionary Computing in the 50’s. Since February 2008 by the birth of Firefly Algorithm, NICs started to receive lots of attentions from researchers around the global. Variants and even new species of NIC algorithms boomed like sprouts after rain. While it may be disputable...
In this paper, two one rank cuckoo search algorithm (ORCSA) based methods are first proposed for solving the short-term hydrothermal scheduling (ST-HTS) problem. The main objective of the ST-HTS problem is to minimize total generation fuel cost over a schedule time while satisfying equality constraints including power balance equations, total water discharge constraint and inequality constraints including...
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