Random-Key Cuckoo Search for the Travelling Salesman Problem

Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random key cuckoo search (RKCS) algorithm for solving the famous Travelling Salesman Problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Levy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.

Bat Algorithm is Better Than Intermittent Search Strategy

The efficiency of any metaheuristic algorithm largely depends on the way of balancing local intensive exploitation and global diverse exploration. Studies show that bat algorithm can provide a good balance between these two key components with superior efficiency. In this paper, we first review some commonly used metaheuristic algorithms, and then compare the performance of bat algorithm with the so-called intermittent search strategy. From simulations, we found that bat algorithm is better than the optimal intermittent search strategy. We also analyse the comparison results and their implications for higher dimensional optimization problems. In addition, we also apply bat algorithm in solving business optimization and engineering design problems.

Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.

Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Optimization

All swarm-intelligence-based optimization algorithms use some stochastic components to increase the diversity of solutions during the search process. Such randomization is often represented in terms of random walks. However, it is not yet clear why some randomization techniques (and thus why some algorithms) may perform better than others for a given set of problems. In this work, we analyze these randomization methods in the context of nature-inspired algorithms. We also use eagle strategy to provide basic observations and relate step sizes and search efficiency using Markov theory. Then, we apply our analysis and observations to solve four design benchmarks, including the designs of a pressure vessel, a speed reducer, a PID controller and a heat exchanger. Our results demonstrate that eagle strategy with L\'evy flights can perform extremely well in reducing the overall computational efforts.

Cuckoo Search: Recent Advances and Applications

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and CS is efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally we propose some important topics for further research.

Computational Optimization, Modelling and Simulation: Recent Trends and Challenges

Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry. Tremendous progress has been observed for all three components over the last few decades. However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired algorithms and surrogate-based techniques for modelling and optimization. This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry. In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges. We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.

Multi-objective Flower Algorithm for Optimization

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.

Bio-Inspired Computation: Success and Challenges of IJBIC

It is now five years since the launch of the International Journal of Bio-Inspired Computation (IJBIC). At the same time, significant new progress has been made in the area of bio-inspired computation. This review paper summarizes the success and achievements of IJBIC in the past five years, and also highlights the challenges and key issues for further research.

True Global Optimality of the Pressure Vessel Design Problem: A Benchmark for Bio-Inspired Optimisation Algorithms

The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimization algorithms. However, its global optimality is not clear and there has been no mathematical proof put forward. In this paper, a detailed mathematical analysis of this problem is provided that proves that 6059.714335048436 is the global minimum. The Lagrange multiplier method is also used as an alternative proof and this method is extended to find the global optimum of a cantilever beam design problem.

Swarm Intelligence Based Algorithms: A Critical Analysis

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems, self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.