Many-Objective Evolutionary Optimisation

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch154 ◽

2011 ◽

pp. 1042-1048

Author(s):

Francesco di Pierro ◽

Soon-Thiam Khu ◽

Dragan A. Savic

Keyword(s):

Evolutionary Algorithms ◽

Detailed Analysis ◽

Critical Review ◽

Research Area ◽

Performance Criteria ◽

Multi Objective ◽

Traditional Remedies ◽

Evolutionary Optimisation ◽

Low Dimensional

Many-objective evolutionary optimisation is a recent research area that is concerned with the optimisation of problems consisting of a large number of performance criteria using evolutionary algorithms. Despite the tremendous development that multi-objective evolutionary algorithms (MOEAs) have undergone over the last decade, studies addressing problems consisting of a large number of objectives are still rare. The main reason is that these problems cause additional challenges with respect to low-dimensional ones. This chapter gives a detailed analysis of these challenges, provides a critical review of the traditional remedies and methods for the evolutionary optimisation of many-objective problems and presents the latest advances in this field.

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Harmony Search-Based Approach for Multi-Objective Software Architecture Reconstruction

Mathematics ◽

10.3390/math8111906 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1906

Author(s):

Amarjeet Prajapati ◽

Zong Woo Geem

Keyword(s):

Evolutionary Algorithms ◽

Software Architecture ◽

Architectural Design ◽

Optimization Problems ◽

Search Algorithm ◽

Harmony Search ◽

Performance Criteria ◽

Software System ◽

Multi Objective

The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.

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Fuzzy multi-objective location and routing problem

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191654 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3259-3273

Author(s):

Nasser Shahsavari-Pour ◽

Najmeh Bahram-Pour ◽

Mojde Kazemi

Keyword(s):

Firefly Algorithm ◽

High Reliability ◽

Research Area ◽

Nsga Ii ◽

Location Routing Problem ◽

Routing Problem ◽

Location Allocation ◽

Multi Objective ◽

Location Routing ◽

The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

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