Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identify some principles behind the metaheuristics’ rules; and we show that ensuring their application, as a correction to a published algorithm for the vertex cover problem, leads to a statistically significant improvement in empiri...

A novel agent-based approach to the configuration of algorithms including, but not limited to, metaheuristics is proposed in this work. Metaheuristics are examples of algorithms where parameters need to be set up as good as possible. Yet the selection of the most adequate parameter values is a recognized arduous work in the metaheuristics community. This paper proposes a new approach that makes...

This short article presents control designs of truck braking system. It describes design comparisons among the simple internal model control (SIMC), Ziegler-Nichol (ZN), Cohen-Coon (CC) methods, and the proposed bacterial-foraging-tabu-search (BFTS) metaheuristics. The proposed BF-TF algorithms are explained. The article also presents simulation results and discussions. Keywords—truck braking c...

Metaheuristics, such as evolutionary algorithms or simulated annealing, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult optimization problems. To show high performance, metaheuristics need to be adapted to the properties of the problem at hand. This paper illustrates how efficient metaheuristics can be developed for com...

In practical applications, one can take advantage of metaheuristics in different ways: To simplify, we can say that metaheuristics can be either used out-of-the-box or a custom version can be developed. The former way requires a rather low effort, and in general allows to obtain fairly good results. The latter implies a larger investment in the design, implementation, and fine-tuning, and can o...

In their search for satisfactory solutions to complex combinatorial problems, metaheuristics methods are expected to intelligently explore the solution space. Various forms of memory have been used to achieve this goal and improve the performance of metaheuristics, which warranted the development of the Adaptive Memory Programming (AMP) framework [1]. This paper follows this framework by integr...

Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The...

In science and engineering, many optimization tasks are difficult to solve, the core concern these days is apply metaheuristic (MH) algorithms solve them. Metaheuristics have gained significant attention in recent years, with nature serving as fundamental inspiration where self-organization property led collective intelligence emerging from behavior of a swarm birds or colony insects more natur...

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.