Multi-objective Ant Colony Optimization Algorithm Research Articles

Multi-objective ant colony optimization for the twin-screw configuration problem

The twin-screw configuration problem (TSCP) consists of identifying the best location of a set of available screw elements along a screw shaft. Owing to its combinatorial nature, it can be seen as a sequencing problem. In addition, different conflicting objectives may have to be considered when defining a screw configuration and, thus, it is usually tackled as a multi-objective optimization problem. In this research, a multi-objective ant colony optimization (MOACO) algorithm was adapted to deal with the TSCP. The influence of different parameters of the MOACO algorithm was studied and its performance was compared with that of a previously proposed multi-objective evolutionary algorithm and a two-phase local search algorithm. The experimental results showed that MOACO algorithms have a significant potential for solving the TSCP.

An improved multi-objective ant colony algorithm for building life cycle energy consumption optimisation

Building energy consumption (BEC) is very important for the environmental sustainability. Because of complexity and variety of building energy consumption, to achieve building energy consumption optimisation, especially for building life-cycle (BLC), multiple objectives have to be satisfied. In this paper, a novel mathematical model for BLC energy consumption assessment is formalised, a novel algorithm for optimisation of BLC energy consumption is developed by improving the multi-objective ant colony optimisation (MACO). In the algorithm, the estimation mechanism of Pareto optimal solution and the update rule of pheromone are derived. An efficacious optimisation solution for BLC energy consumption and an innovative application of MACO algorithm in the building energy efficiency area are presented in the paper.

HCHAC: A family of MOACO algorithms for the resolution of the bi-criteria military unit pathfinding problem

This paper presents a family of Multi-Objective Ant Colony Optimization (MOACO) algorithms, globally identified as hCHAC, which have been designed to solve a pathfinding problem in a military context considering two objectives: maximization of speed and safety. Each one of these objectives include different factors (such as stealth or avoidance of resource-consuming zones), that is why in this paper we generate different members of the hCHAC family by aggregating the initial cost functions into a different amount of objectives (from one to four) and considering a different parametrization set in each case. The hCHAC algorithms have been tested in several different (and increasingly realistic) scenarios, modelled in a simulator and compared with some other well-known MOACOs. These latter algorithms have been adapted for the purpose of this work to deal with this problem, along with a new multi-objective greedy approach that has been included as baseline for comparisons. The experiments show that most of the hCHAC algorithms outperform the other approaches, yielding at the same time very good military behaviour in the tactical sense. Within the hCHAC family, hCHAC-2, an approach considering two objectives, yields the best results overall.

Mixed-model assembly line balancing using a multi-objective ant colony optimization approach

Mixed-model assembly lines are production systems at which two or more models are assembled sequentially at the same line. For optimal productivity and efficiency, during the design of these lines, the work to be done at stations must be well balanced satisfying the constraints such as time, space and location. This paper deals with the mixed-model assembly line balancing problem (MALBP). The most common objective for this problem is to minimize the number of stations for a given cycle time. However, the problem of capacity utilization and the discrepancies among station times due to operation time variations are of design concerns together with the number of stations, the line efficiency and the smooth production. A multi-objective ant colony optimization (MOACO) algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the MOACO algorithm is an efficient and effective algorithm which gives better results than other methods compared.

Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different Nissan scenarios

Most of the decision support systems for balancing industrial assembly lines are designed to report a huge number of possible line configurations, according to several criteria. In this contribution, we tackle a more realistic variant of the classical assembly line problem formulation, time and space assembly line balancing. Our goal is to study the influence of incorporating user preferences based on Nissan automotive domain knowledge to guide the multi-objective search process with two different aims. First, to reduce the number of equally preferred assembly line configurations (i.e., solutions in the decision space) according to Nissan plants requirements. Second, to only provide the plant managers with configurations of their contextual interest in the objective space (i.e., solutions within their preferred Pareto front region) based on real-world economical variables. We face the said problem with a multi-objective ant colony optimisation algorithm. Using the real data of the Nissan Pathfinder engine, a solid empirical study is carried out to obtain the most useful solutions for the decision makers in six different Nissan scenarios around the world.

A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.

CHAC, A MOACO algorithm for computation of bi-criteria military unit path in the battlefield: Presentation and first results

In this paper, we present a multiobjective ant colony optimization (MOACO) algorithm, called CHAC, designed to solve the problem of finding the path for a military unit that minimizes the cost in resources while maximizing safety. Unlike previous MOACO algorithms, CHAC uses a single colony and two different state transition rules: One that combines the heuristic and pheromone information of both objectives and another based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in different scenarios (maps with different degrees of difficulty), outperforming a greedy algorithm (taken as baseline), and yielding a good military behavior in the tactical sense. In comparison, the combined rule is slightly better than the rule based on dominance. © 2009 Wiley Periodicals, Inc.

Optimisation multi-objectif pour le problème de dimensionnement de buffers

In this paper, we are studying the buffer sizing problem in production lines. It is one of the most studied subjects in the literature with one criterion but shows a lack of studies in its multiobjective form. Therefore, we present two multiobjective optimization techniques to solve the problem and which are: the second version of the Strength Pareto Evolutionary Algorithm (SPEA2) and a multiobjective ant colony optimization algorithm. Three measures are used in order to compare the performances of the two developed algorithms. The algorithms are then applied for buffers sizing in a packaging line. A simulation model with the ARENA software is used for the performances evaluations. The simulation model is coupled with the optimization algorithms applying then the simulation based optimization technique. The numerical results of the algorithm based on ant colony are very relevant and show an advantage compared to the SPEA2.