The flow chart of CGMA. The 0-1 knapsack problem is a classic combinational optimization problem.The monkey algorithm (MA) is a novel swarm intelligent based algorithm.This paper proposed a binary version of the monkey algorithm for solving 0-1 knapsack problem.The result of the proposed algorithm provides better results in solving the 0-1 knapsack problem compared with the other solving methods. The 0-1 knapsack problem is a classic combinational optimization problem. However, many exiting algorithms have low precision and easily fall into local optimal solutions to solve the 0-1 knapsack problem. In order to overcome these problems, this paper proposes a binary version of the monkey algorithm where the greedy algorithm is used to strengthen the local search ability, the somersault process is modified to avoid falling into local optimal solutions, and the cooperation process is adopted to speed up the convergence rate of the algorithm. To validate the efficiency of the proposed algorithm, experiments are carried out with various data instances of 0-1 knapsack problems and the results are compared with those of five metaheuristic algorithms.

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An improved monkey algorithm for a 0-1 knapsack problem

The Travelling Salesman Problem (TSP) is one of the most well-known combinatorial optimization problems and has attracted a lot of interests from researchers. Many studies have proposed various methods for solving the two-dimensional TSP. In this study, we extend the two-dimensional TSP to the three-dimensional TSP, namely the spherical TSP in which all points (cities) and paths (solutions) are on the surface of a sphere. A hybrid algorithm based on the glowworm swarm optimization (GSO) and the complete 2-opt algorithm is proposed, in which the carriers of the luciferin are transformed from glowworms to edges between cities, and the probabilistic formula and the luciferin updating formula are modified. In addition, the complete 2-opt algorithm is performed to optimize the selected optimal routes every few iterations. Numerical experimental results show that the proposed algorithm has a better performance than the basic GSO in solving the spherical TSP. Meanwhile, the complete 2-opt algorithm can speed up the convergence rate.

A hybrid algorithm combining glowworm swarm optimization and complete 2-opt algorithm for spherical travelling salesman problems

This paper addresses the problem of localizing the sources of contaminants spread in the environment, and mapping the boundary of the affected region using an innovative swarm intelligence based technique. Unlike most work in this area the algorithm is capable of localizing multiple sources simultaneously while also mapping the boundary of the contaminant spread. At the same time the algorithm is suitable for implementation using a mobile robotic sensor network. Two types of agents, called the source localization agents (or S-agents) and boundary mapping agents (or B-agents) are used for this purpose. The paper uses the basic glowworm swarm optimization (GSO) algorithm, which has been used only for multiple signal source localization, and modifies it considerably to make it suitable for both these tasks. This requires the definition of new behaviour patterns for the agents based on their terminal performance as well as interactions between them that helps the swarm to split into subgroups easily and identify contaminant sources as well as spread along the boundary to map its full length. Simulations results are given to demonstrate the efficacy of the algorithm.

Simultaneous source localization and boundary mapping for contaminants

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers.

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Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

The 0–1 knapsack problem (0–1KP) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays significant roles in many real life applications. Dragonfly algorithm (DA) a novel swarm intelligence optimization algorithm, inspired by the nature of static and dynamic swarming behaviors of dragonflies. DA has demonstrated excellent performance in solving multimodal continuous problems and engineering optimization problems. This paper proposes a binary version of dragonfly algorithm (BDA) to solve 0–1 knapsack problem. Experimental results have proven the superior performance of BDA compared with other algorithms in literature.

Solving 0–1 Knapsack Problems by Binary Dragonfly Algorithm

According to the principle of swarm intelligence,this paper proposed a new optimization algorithm based on the ideas of glowworms:the glowworm swarm optimization(GSO) algorithm to solve the 0-1 knapsack problem.Through the numerical simulations,it compared with that of artificial bee colony algorithm,ant colony optimization algorithm and particle swarm optimization.And it obtains the satisfactory results,which show the validity and effectiveness of the algorithm,expands the applications of GSO.

Ma Liang

Papers

Artificial glowworm swarm optimization algorithm for 0-1 knapsack problem