Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligen...

This paper proposes an approximate optimization algorithm combining P systems with ant colony optimization, called ACOPS, to solve traveling salesman problems, which are well-known and extensively studied NP-complete combinatorial optimization problems. ACOPS uses the pheromone model and pheromone update rules defined by ant colony optimization algorithms, and the hierarchical membrane structur...

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages long running time and easy stagnation still restrict its further wide application many fields. In this study, saltatory evolution (SEACO) is proposed to incr...

One of the challenges mutation testing is fixing faults. In debugging phase, all live mutants were repaired. Programs need high scores to be declared reliable program codes. Each test can allow identification multiple mutants. This what confuses faults process. The objective this research get shortest route so that it help in sorting mutant types during application improvement after testing. op...

This paper proposes a novel multi-objective ant colony system (MOACS) approach to solve the cooperative task allocation problem of multi-robot systems. The is formulated as multiple traveling salesman (MTSP). objectives are minimize total and maximum cost robotic vehicles so that workload each vehicle could be balanced. time matrices salesmen different asymmetric due flight speeds executing tas...

in this study, a two-objective mixed-integer linear programming model (milp) for multi-product re-entrant flow shop scheduling problem has been designed. as a result, two objectives are considered. one of them is maximization of the production rate and the other is the minimization of processing time. the system has m stations and can process several products in a moment. the re-entrant flow sho...

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation networkwithin a stadium as a hierarchical directed network.We propose amult...

The Learnable Ant Colony Optimization (LACO) is proposed to satellite ground station system scheduling problems. The LACO employs an integrated modelling idea which combines the ant colony model with the knowledge model. In order to improve the performance, LACO largely pursues the complementary advantages of ant colony model and knowledge model. Experimental results suggest that LACO is a feas...

Hybrid algorithm is proposed to solve combinatorial optimization problem by using Ant Colony and Genetic programming algorithms. Evolutionary process of Ant Colony Optimization algorithm adapts genetic operations to enhance ant movement towards solution state. The algorithm converges to the optimal final solution, by accumulating the most effective sub-solutions.

This paper presents an ant colony optimization based algorithm to solve real parameter optimization problems. In the proposed method, an operation similar to the crossover of the genetic algorithm is introduced into the ant colony optimization. The crossover operation with Laplace distribution following a few promising descent directions (FPDD-LX) is proposed to be performed on the pheromone of...