Related papers: DeepACO: Neural-enhanced Ant Systems for Combinato…
Beam-ACO, a modification of the traditional Ant Colony Optimization (ACO) algorithms that incorporates a modified beam search, is one of the most effective ACO algorithms for solving the Traveling Salesman Problem (TSP). Although adding…
Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithm overcoming some limitations of the traditional ACO algorithm. However,due to…
A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of…
In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing…
Taking inspiration from nature for meta-heuristics has proven popular and relatively successful. Many are inspired by the collective intelligence exhibited by insects, fish and birds. However, there is a question over their scalability to…
Stabilizing the complexity of Feedforward Neural Networks (FNNs) for the given approximation task can be managed by defining an appropriate model magnitude which is also greatly correlated with the generalization quality and computational…
Applications of ACO algorithms to obtain better solutions for combinatorial optimization problems have become very popular in recent years. In ACO algorithms, group of agents repeatedly perform well defined actions and collaborate with…
This paper discusses the problem of placing weighted items in a circular container in two-dimensional space. This problem is of great practical significance in various mechanical engineering domains, such as the design of communication…
This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to solve challenging optimization problems that has nonlinear relationship between resource consumption…
This paper introduces the Random-Key Optimizer (RKO), a versatile and efficient stochastic local search method tailored for combinatorial optimization problems. Using the random-key concept, RKO encodes solutions as vectors of random keys…
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuristic algorithms which are usually fast but may sacrifice the…
Finding a feasible and prompt solution to the Vehicle Routing Problem (VRP) is a prerequisite for efficient freight transportation, seamless logistics, and sustainable mobility. Traditional optimization methods reach their limits when…
To construct a robot that can walk as efficiently and steadily as humans or other legged animals, we develop an enhanced elitist-mutated ant colony optimization~(EACO) algorithm with genetic and crossover operators in real-time applications…
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Despite their potential, existing NCO methods often suffer from…
Audio, animations and video belong to a class of data known as delay sensitive because they are sensitive to delays in presentation to the users. Also, because of huge data in such items, disk is an important device in managing them. In…
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search…
Ant Colony Optimization (ACO) is a swarm intelligence methodology utilized for solving optimization problems through information transmission mediated by pheromones. As ants sequentially secrete pheromones that subsequently evaporate, the…
In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in the context of the transit network design problem, a uniquely challenging combinatorial optimization…
This study introduces an innovative methodology for the planning of metro network routes within the urban environment of Chennai, Tamil Nadu, India. A comparative analysis of the modified Ant Colony Optimization (ACO) method (previously…
Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting…