Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

A multi-objective ant colony optimization algorithm based on the Physarum-inspired mathematical model

Abstract: Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.

An ant colony system solving the travelling salesman region problem

Abstract: A variant of the well-known travelling salesman problem is about introducing particular dependencies among the cities. Such dependencies might describe the relations between single cities which can be used for autonomous vehicles that need to follow certain paths for some reasons. This paper deals with solving the Travelling Salesman Region Problem where particular connections of cities are already predefined. We are investigating and discussing the influences of the parameters of the Ant Colony Optimization algorithm solving travelling salesman region problems.

Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Abstract: The traveling salesman problem (TSP), and the quadratic assignment problem (QAP) are two combinatorial optimization problems with a diverse set of applications. This research paper aims to apply the adaptation of discrete cat swarm optimization algorithm inspired by the natural behavior of cats to solve TSP and QAP. Simulated experiments were conducted on several benchmark instances taken from the OR-library. The results show the effectiveness of the proposed adaptation to solve real applications area based on the two-studied problems.

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem.

Abstract: This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

Multi-objective Topology Optimization with Ant Colony Optimization and Genetic Algorithms

Abstract: In this work, we present a multi-objective approach for Topology Optimization applied to the design of devices with several materials. The first stage consists of applying a Multi-Objective Ant Colony Optimization (MOACO) to find tradeoff topologies with different material distributions. In the second stage, we parameterize the boundaries of the topologies found by using NURBS. A Multi-objective Genetic Algorithm is applied as a heuristic optimization engine to optimize the control points, weights and knots of the curves in order to smooth and refine the boundaries of the topology. The main advantage of a multi-objective approach is that the designer can identify, explore and refine a number of tradeoff topologies. The proposed methodology is illustrated in the design of a C-core magnetic actuator.