Markov Chain-based Clustering Analysis of Customers and WebPages

This paper focuses on users’ behavior towards an EC website. A novel Markov Chain-based way combining the web log file information and the topology of an EC website is presented to rank a user\u27s interest in a WebPage. Then a URL-USERID relevant matrix is set up, with URL taken as a row and USERID as column, and each element’s value is the probability of a user to access a WebPage when time goes infinitely. The similarity of each column vector can be used to cluster customers, and relevant web pages can be found from the similarity of each row vector. The knowledge discovered by this dynamic model can be fairly helpful to the design and maintenance of a website, to provide personalized service, and can be used in an effective recommending system of an EC website etc

Dichotomous Binary Differential Evolution for Knapsack Problems

Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to adapt into combinatorial optimization problems without sacrificing the original evolution mechanism of DE is harder work to the researchers to design an efficient binary differential evolution (BDE). To tackle this problem, this paper presents a novel BDE based on dichotomous mechanism for knapsack problems, called DBDE, in which two new proposed methods (i.e., dichotomous mutation and dichotomous crossover) are employed. DBDE almost has any difference with original DE and no additional module or computation has been introduced. The experimental studies have been conducted on a suite of 0-1 knapsack problems and multidimensional knapsack problems. Experimental results have verified the quality and effectiveness of DBDE. Comparison with three state-of-the-art BDE variants and other two state-of-the-art binary particle swarm optimization (PSO) algorithms has proved that DBDE is a new competitive algorithm

An Enhanced Differential Evolution with Elite Chaotic Local Search

Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions

OPTIMIZING THE INVENTORY ROUTING PROBLEM USING ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHM

The inventory routing problem (IRP) is an NP-hard optimization problem, which integrally seeks the minimal cost of inventory and transportation in supply chain management. However, the inventory management activities and transportation activities are conflicting in most cases. Therefore, how to efficiently solve the one-to-many IRP is still a challenge and practical requirement. As a powerful and popular evolutionary algorithm, differential evolution (DE) has been successfully applied to tackle complex industry optimization problems. In this paper, a novel method for the one-to-many IRP using an adaptive DE algorithm (named aDEIRP) is proposed. In the proposed aDEIRP algorithm, the DE algorithm with the deliver routing-based solution representation is used to solve the complex one-to-many IRP problem. In addition, a modified DE with adaptive parameter control is proposed to enhance the optimization performance. Comprehensive experimental results verified the effectiveness and efficiency of the proposed method