Minxia Zhang

A hybrid biogeography-based optimization and fireworks algorithm

The paper presents a hybrid biogeography-based optimization (BBO) and fireworks algorithm (FWA) for global optimization. The key idea is to introduce the migration operator of BBO to FWA, in order to enhance information sharing among the population, and thus improve solution diversity and avoid premature convergence. A migration probability is designed to integrate the migration of BBO and the normal explosion operator of FWA, which can not only reduce the computational burden, but also achieve a better balance between solution diversification and intensification. The Gaussian explosion of the enhanced FWA (EFWA) is reserved to keep the high exploration ability of the algorithm. Experimental results on selected benchmark functions show that the hybrid BBO FWA has a significantly performance improvement in comparison with both BBO and EFWA.

Bio-Inspired Meta-Heuristics for Emergency Transportation Problems

Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), etc., for solving emergency transportation problems. We then propose a new hybrid biogeography-based optimization (BBO) algorithm, which outperforms some state-of-the-art heuristics on a typical transportation planning problem.

Improving Enhanced Fireworks Algorithm with New Gaussian Explosion and Population Selection Strategies

Fireworks algorithm (FWA) is a relatively new metaheuristic in swarm intelligence and EFWA is an enhanced version of FWA. This paper presents a new improved method, named IEFWA, which modifies EFWA in two aspects: a new Gaussian explosion operator that enables new sparks to learn from more exemplars in the population and thus improves solution diversity and avoids being trapped in local optima, and a new population selection strategy that enables high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. Numerical experiments show that the IEFWA algorithm outperforms EFWA on a set of benchmark function optimization problems.

Fireworks Algorithm with Enhanced Fireworks Interaction

As a relatively new metaheuristic in swarm intelligence, fireworks algorithm (FWA) has exhibited promising performance on a wide range of optimization problems. This paper aims to improve FWA by enhancing fireworks interaction in three aspects: 1) Developing a new Gaussian mutation operator to make sparks learn from more exemplars; 2) Integrating the regular explosion operator of FWA with the migration operator of biogeography-based optimization (BBO) to increase information sharing; 3) Adopting a new population selection strategy that enables high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. The combination of the three strategies can significantly enhance fireworks interaction and thus improve solution diversity and suppress premature convergence. Numerical experiments on the CEC 2015 single-objective optimization test problems show the effectiveness of the proposed algorithm. The application to a high-speed train scheduling problem also demonstrates its feasibility in real-world optimization problems.

Biogeographic harmony search for emergency air transportation

Harmony search (HS) and biogeography-based optimization (BBO) are two metaheuristic optimization methods which have demonstrated effectiveness on a wide variety of optimization problems. The paper proposes a new hybrid biogeographic harmony search (BHS) method, which integrates the blended migration operator of BBO with HS to enrich harmony diversity, and thus achieves a much better balance between exploration and exploitation. We then apply the BHS method to an emergency air transportation problem, and show that the proposed method is very competitive with the state-of-the-art BBO, HS, and other comparative algorithms on a set of problem instances from real-world disaster relief operations in China.

A Water Wave Optimization Algorithm with Variable Population Size and Comprehensive Learning

Water wave optimization (WWO) is a new nature-inspired metaheuristic by mimicking shallow water wave motions including propagation, refraction, and breaking. In this paper we present a variation of WWO, named VC-WWO, which adopts a variable population size to accelerate the search process, and develops a comprehensive learning mechanism in the refraction operator to make stationary waves learn from more exemplars to increase the solution diversity, and thus provides a much better tradeoff between exploration and exploitation. Experimental results show that the overall performance of VC-WWO is better than the original WWO and other comparative algorithms on the CEC 2015 single-objective optimization test problems, which validates the effectiveness of the two new strategies proposed in the paper.

University course timetabling using a new ecogeography-based optimization algorithm

As an important administrative task in the area of education, course timetabling is a complex optimization problem that is difficult to solve by conventional methods. The paper adapts a new nature-inspired metaheuristic called ecogeography-based optimization (EBO), which enhances biogeography-based optimization by equipping the population with a neighborhood structure and designing two new migration operators named global migration and local migration, to solve the university course timetabling problem (UCTP). In particular, we develop two discrete migration operators for efficiently evolving UCTP solutions based on the principle of global and local migration in EBO, and design a repair process for effectively coping with infeasible timetables. We test the discrete EBO algorithm on a set of problem instances from four universities in China, and the experimental results show that the proposed method exhibits a promising performance advantage over a number of state-of-the-art methods.

A Hyper-Heuristic Method for UAV Search Planning.

Motivated by the wide use of unmanned aerial vehicles (UAV) in search-and-rescue operations, we consider a problem of planning the search sequence and search modes of UAV, the aim of which is to maximize the probability of finding the target in a complex environment with probabilistic belief of target location. We design five meta-heuristic algorithm for solving the complex problem, but find that none of them can always obtain satisfactory solutions on a variety of instances. To overcome this obstacle, we integrate these meta-heuristics into a hyper-heuristic framework, which adaptively manage the low-level heuristics (LLH) by using feedback of their real-time performance in problem solving, and thus can find the most suitable LLH or their combination that can outperform any single LLH on each given instance. Experiments show that the overall performance of the hyper-heuristic is significantly better than any individual heuristic on the test instances.

A Discrete Ecogeography-Based Optimization Algorithm for University Course Timetabling

Ecogeography-based optimization (EBO) is an enhanced version of biogeography-based optimization (BBO) algorithm borrowing ideas from island biogeographic evolution for global optimization. The paper proposes a discrete EBO algorithm for university course timetabling problem (UCTP). We first present the mathematical model of UCTP, and then design specified global and local migration operators for the problem. Computational experiment shows that the proposed algorithm exhibits a promising performance on a set of test problem instances.

Application of Biogeography-Based Optimization in Transportation

There are a lot of optimization problems in the field of transportation, some of which can be modeled as continuous optimization problems, while others can be modeled as combinatorial optimization problems. Nowadays, with the development of transportation systems, most of such problems are high-dimensional and/or NP-hard. In recent years, we have adapted BBO algorithm to a variety of transportation problems and achieved good results.