Lifecycle-based Swarm Optimization Method for Constrained Optimization

Abstract

Abstract-Each biologic must go through a process from birth, growth, reproduction until death, this process known as life cycle. This paper borrows the biologic life cycle theory to propose a Lifecycle-based Swarm Optimization (LSO) algorithm. Based on some features of life cycle, LSO designs six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In this paper, the capability of the LSO to address constrained optimization problem was investigated. Firstly, the proposed method was test on some well-known and widely used benchmark problems. When compared with PSO, we can see that LSO can obtain the better solution and lower standard deviation than PSO on many different types of constrained optimization problems. Finally, LSO was also used for seeking the optimal route for vehicle route problem in logistics system. The result of LSO is the best when comparing with PSO and GA. The results of above two types of experiments, which include not only the ordinary benchmark problem but also the practical problems in engineering, demonstrate that LSO is a competitive and effective approach for solving constrained problems.

Lifecycle-based swarm optimization method for multi-objective optimization problem (MOP)

“…RESULTS ANALYSIS Table I contains the experimental results of two algorithms for five testing functions based on the two evaluation index, the values in table are the mean that algorithm running independently 30 times. Figure 1,2,3,4 are the graphical solving results of two algorithm for the above four functions, and abscissa represents objective function 1 f , ordinate shows objective function 2 f . First, from the graphics, NLSO is superior to NSGA.…”

“…Then 7 unimodal unconstrained optimization test functions, and constrained optimization test functions, and engineering problems included Vehicle Routing Problem (VRP) problem and Vehicle Routing Problem with Time Windows (VRPTW) problem, were adopt to test LSO algorithm performance [1][2][3]. Above experiments demonstrate that LSO is a competitive and effective approach.…”

“…In 2011, borrowing the biologic life cycle theory, the Lifecycle-based Swarm Optimization (LSO) algorithm was proposed for the first time [1]. Then 7 unimodal unconstrained optimization test functions, and constrained optimization test functions, and engineering problems included Vehicle Routing Problem (VRP) problem and Vehicle Routing Problem with Time Windows (VRPTW) problem, were adopt to test LSO algorithm performance [1][2][3].…”

Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-swarm Cooperative Chaos Particle Swarm Optimization and Its Application

“…Step 4: Calculate the inertia weight of each chaos slave swarm according to (17) and (18). Select the best particle of each chaos slave swarm, Update the particle velocity and position of slave swarms, and complete the adaptive mutation of the slave swarms according to the (19).The fitness function is defined as the 1-M cc of the MCCPSO algorithm method on the training data set, which is shown in (20) .the solution with a bigger fitness, has a smaller fitness value.…”

“…Particle swarm optimization (PSO) algorithm is a heuristic global search algorithm, which has been successfully applied in many problems such as multiobjective optimization [17], fault diagnosis [18], automatic target detection [19], mechanical design [20], neural network training [21], pattern recognition [22], signal processing [23], speech recognition [24], and robotics and time-frequency analysis with its good performance [25]. But standard PSO tends to search premature solutions in optimization problems.…”